Automated Program Repair (APR) is increasingly vital for managing the complexity of modern software systems. However, current APR techniques suffer from inefficiently selecting repair components, resulting in suboptimal patches. To address these limitations, we propose PCRepair, a context-aware template-based methodology for automated software fault repair. This approach integrates predefined repair templates with context-aware analysis to improve repair accuracy and efficiency. PCRepair first localizes suspicious statements via the Ochiai technique, then matches their contextual patterns with relevant templates. This strategy narrows the search space and generates semantically relevant candidate patches. We prioritize these patches using a weighted fusion similarity metric and sequentially validate them against existing test cases. Evaluations on the Defects4J benchmark show that PCRepair successfully repaired 38 defects, demonstrating competitive performance compared to existing methods, particularly in terms of repair efficiency, with a 9.62% success rate and an average repair time of fewer than 30 min per defect.

Local spectral features and global spatial context are essential for hyperspectral image (HSI) classification. However, existing methods based on convolutional neural networks (CNNs), graph convolutional networks (GCNs), and Transformers often rely on multibranch structures to separately extract and fuse local and global features, resulting in high computational complexity and redundant information that can negatively affect classification performance. To address these issues, we propose a two-stage graph convolutional mamba network (TGMN) that enables efficient modeling of local and global features through sequential intrasubgraph local feature extraction and intersubgraph global information learning. Specifically, in the first stage, we partition the HSI into superpixel regions and treat each superpixel as a subgraph, where a GCN is applied to aggregate spectral-spatial features within each subgraph. We further design a downsampled subgraph feature reconstruction (DSFR) module that dynamically selects key nodes to reduce redundancy, highlight critical features, and enhance model representation capability. In the second stage, the Mamba network models the global dependencies between subgraphs and introduces a region-relation aware absolute positional encoding (RAPE) module. This module encodes spatial positional information into embedded vectors by integrating the relative distance and direction between the geometric center of each superpixel and the image center, which are then deeply fused with the feature matrix to improve spatial relationship comprehension. The two-stage sequential structure ensures effective local and global feature extraction, avoiding the high computational complexity and redundancy issues commonly associated with multibranch models. Experiments on three benchmark datasets demonstrate its superiority, achieving classification accuracies of 98.54%, 98.30%, and 96.94% on the Indian Pines, Dioni, and Honghu datasets, respectively. Compared to state-of-the-art methods, TGMN achieves higher classification accuracy with significantly lower computational cost, demonstrating its efficiency and effectiveness for HSI classification.

Software defect prediction (SDP) is an effective approach to ensure software reliability. Machine learning models have been widely employed in SDP, but they ignore the impact of class imbalance, noise and outliers on the prediction performance. This study proposes a fuzzy cost broad learning system (FC‐BLS). FC‐BLS not only handles class imbalance problems but also considers the specific sample distribution to address noise and outliers in software defect datasets. Our approach draws fully on the idea of the cost matrix and fuzzy membership functions. It introduces them to BLS, where the cost matrix prioritises the training errors on the minority samples. Hence, the classification hyperplane position is more reasonable, and fuzzy membership functions calculate the membership degree of the sample in a feature mapping space to remove the prediction error caused by noise and outlier samples. Then, the optimisation problem is constructed based on the idea that the minority class and normal instances have relatively high costs. By contrast, the majority class and noise and outlier instances have relatively small costs. This study conducted experiments on nine NASA SDP datasets, and the experimental findings demonstrated the effectiveness of the proposed methodology on most datasets.

With the increasing scale and complexity of software recently, automated software bug repair has grown in importance. However, the current automated software bug repair process suffers from issues such as coarse-grained repair granularity and poor patch quality. To address these problems, we propose a graph mining template-based automatic software repair (GMRepair) to improve the performance of automated software bug repair. First, this approach adopts the Ochiai fault localization technique to locate and generate a list of suspicious defect statements. We utilize the GumTree tool to parse the bug and repair program files, generating edit scripts. These edit scripts are then transformed into a graphical representation. Second, we utilize a frequent graph miner to obtain graph mining templates by matching the context of the suspicious statements with the context of the graph mining templates, generating an initial population for them. The buggy program is evolved using genetic programming through mutation and crossover operations, generating new individuals. Finally, we sequentially pass the candidate patches (CPs) through corresponding test cases and prioritize the test cases using priority sorting techniques. Patches that fail to pass the test cases are filtered out, and the patches that pass the test cases are output. We conducted the experiments using two datasets, QuixBugs and Defects4J. In Defects4J, the GMRepair successfully repaired 41 defects, while in QuixBugs, it successfully repaired 15 defects. Compared to the existing methods, GMRepair offers a higher success rate and efficiency in defect repair.

Recently, contrastive learning (CL) has shown excellent performance in hyperspectral image (HSI) classification. However, existing CL based methods face two specific challenges. (1) Multi-view samples inevitably introduce ambiguity and uncertainty due to data augmentation operations. Traditional contrastive learning methods fail to effectively model these dynamic ambiguous features, resulting in a lack of robustness in the feature learning process. (2) Existing CL based methods primarily learns feature representations by pulling positive samples closer and pushing negative samples apart. But, they lack structured modeling of intra-class feature compactness and inter-class feature separability. To address these challenges, we propose a fuzzy triplet contrastive learning (FTCL) method for HSI classification. For the first challenge, we propose a multi-view fuzzy neighborhood learning (MFNL) module. This module effectively models the ambiguity among multi-view samples through fuzzy membership calculation, multi-view fuzzy weight matrix generation, and weighted feature aggregation, significantly enhancing the robustness and stability of feature representations. To tackle the second challenge, we design a triplet feature discriminative (TFD) classifier, which improves intra-class compactness by minimizing the distance between anchor samples and positive samples, while enhancing inter-class separability by maximizing the distance between anchor samples and negative samples. This enables precise modeling of intra-class compactness and inter-class separability. The proposed method is evaluated on four HSI datasets, and the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.

BACKGROUND: Electronic medical records (EMR) contain detailed information about patient health. Developing an effective representation model is of great significance for the downstream applications of EMR. However, processing data directly is difficult because EMR data has such characteristics as incompleteness, unstructure and redundancy. Therefore, preprocess of the original data is the key step of EMR data mining. The classic distributed word representations ignore the geometric feature of the word vectors for the representation of EMR data, which often underestimate the similarities between similar words and overestimate the similarities between distant words. This results in word similarity obtained from embedding models being inconsistent with human judgment and much valuable medical information being lost. RESULTS: In this study, we propose a biomedical word embedding framework based on manifold subspace. Our proposed model first obtains the word vector representations of the EMR data, and then re-embeds the word vector in the manifold subspace. We develop an efficient optimization algorithm with neighborhood preserving embedding based on manifold optimization. To verify the algorithm presented in this study, we perform experiments on intrinsic evaluation and external classification tasks, and the experimental results demonstrate its advantages over other baseline methods. CONCLUSIONS: Manifold learning subspace embedding can enhance the representation of distributed word representations in electronic medical record texts. Reduce the difficulty for researchers to process unstructured electronic medical record text data, which has certain biomedical research value.

Detecting adverse drug events from Twitter is expected to reveal unreported side effects, thereby complementing current spontaneous reporting systems. However, existing studies usually only use word embeddings as the input for deep learning models, which ignores the structural information of sentences. In addition, deep learning models usually require a large number of cases for training, but the scale of annotated corpora that can be used for this task is limited. In order to solve the above problems, we propose a graph-boosted framework, that constructs the text into a graph structure. By using pre-trained graph embeddings and word embeddings for model training, our proposed framework provides richer semantic and structural information for prediction. The experimental results show that the proposed method can be used in different deep learning models and bring improvements when using the TwiMed corpus of different scales.

Pre-trained distributed word representations have been proven useful in various natural language processing (NLP) tasks. However, the effect of words’ geometric structure on word representations has not been carefully studied yet. The existing word representations methods underestimate the words whose distances are close in the Euclidean space, while overestimating words with a much greater distance. In this paper, we propose a word vector refinement model to correct the pre-trained word embedding, which brings the similarity of words in Euclidean space closer to word semantics by using manifold learning. This approach is theoretically founded in the metric recovery paradigm. Our word representations have been evaluated on a variety of lexical-level intrinsic tasks (semantic relatedness, semantic similarity) and the experimental results show that the proposed model outperforms several popular word representations approaches.