In recent years, collaborative classification of multimodal data, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR), has been widely used to improve remote sensing image classification accuracy. However, existing fusion approaches for HSI and LiDAR suffer from limitations. Fusing the heterogeneous features of HSI and LiDAR proved to be challenging, leading to incomplete utilization of information for category representation. Additionally, during the extraction of spatial features from HSI, the spectral and spatial information are often disjointed. It leads to the difficulty of fully exploiting the rich spectral information in hyperspectral data. To address these issues, we proposed a multimodal data fusion framework specifically designed for HSI and LiDAR fusion classification, called Modality Fusion Vision Transformer (MFViT). We have designed a stackable modality fusion block (MFB) as the core of our model. Specifically, these blocks mainly consist of multimodal cross-attention modules and spectral self-attention modules. The proposed novel multimodal cross-attention module for feature fusion addresses the issue of insufficient fusion of heterogeneous features from HSI and LiDAR for category representation. Compared to other cross-attention methods, it reduces the alignment requirements between modal feature spaces during cross-modal fusion. The spectral self-attention module can preserve spatial features while exploiting the rich spectral information and participating in the process of extracting spatial features from HSI. Ultimately, we achieve overall classification accuracies of 99.91%, 99.59%, and 96.98% on three benchmark datasets respectively, surpassing all state-of-the-art methods, demonstrating the stability and effectiveness of our model.

The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.

Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.

Along with the development of Internet of Things systems, numerous edge intelligent devices can obtain a large amount of user data, and the analysis of this user data can be applied to business scenarios, such as user click-through rate (CTR) prediction. In the recommendation, advertising and other scenarios, the users at the edge have high response requirements for CTR prediction model training and inference. In the current edge scenario of CTR prediction, there are problems of overly complex model structure and highly sparse original features, which makes it difficult to deploy CTR prediction models at the edge. Therefore, we propose KD-based graph attention FI model (KD-GAFIM), a lightweight recommendation algorithm that combines graph neural networks (GNNs) with knowledge distillation (KD). The approach uses graph attention networks to flexibly capture feature dependencies in a way that maintains a small model size while augmenting the feature vector with feature dependencies. And by sharing the embedding layer of the teacher model, KD-GAFIM improves the efficiency of user CTR prediction. On top of that, we also propose a feature compression strategy guided by model interpretability, which identifies high-contributing features for inference and model refinement based on their performance in model interpretability. This strategy improves efficiency, making KD-GAFIM suitable for training and inference on edge devices. We conducted extensive experiments on multiple datasets. The experimental results show that KD-GAFIM outperforms various state-of-the-art CTR prediction models, demonstrating that GNN-based KD models can improve model performance while reducing model size and feature dimensionality, and have significant potential for application at the edge.

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs.