Context: Log anomaly detection is critical for maintaining the security, stability, and operational efficiency of modern software systems, especially as they generate vast and diverse log data. However, existing deep learning models struggle with the challenges of heterogeneous log formats across systems and the scarcity of labeled anomaly logs, limiting their real-world deployment and generalization capabilities. Objective: To address these challenges, we propose LogMeta, a novel semi-supervised framework designed for adaptive and efficient log anomaly detection in diverse and low-resource environments. Method: LogMeta integrates Model-Agnostic Meta-Learning (MAML) with a hybrid language model to address key challenges. MAML enables LogMeta to rapidly adapt to unseen log systems using few-shot samples, while the hybrid model combines RoBERTa for extracting semantic representations with Bi-LSTM and attention mechanisms to capture sequential dependencies and critical features within log sequences. This design reduces reliance on large-scale labeled datasets and enhances adaptability in heterogeneous environments. Results: Experimental evaluations on multiple benchmark datasets demonstrate that LogMeta consistently outperforms state-of-the-art supervised and unsupervised methods, achieving up to a 28.3% improvement in F1-scores under low-resource scenarios compared to other models. Furthermore, LogMeta exhibits exceptional domain transfer capabilities, maintaining robust performance across diverse log datasets with minimal fine-tuning. In terms of efficiency, LogMeta achieves competitive training and inference times, making it suitable for real-time anomaly detection in large-scale systems. Conclusion: LogMeta provides a scalable and practical solution for real-world log anomaly detection, overcoming challenges related to data heterogeneity and label scarcity. Its strong generalization capabilities, minimal supervision requirements, and adaptability to new log systems make it a promising tool for enhancing software system reliability and security. © 2026 The Author(s).

Software systems have been evolving rapidly and inevitably introducing bugs at an increasing rate, leading to significant maintenance costs. While large language models (LLMs) have demonstrated remarkable potential in enhancing software development and maintenance practices, particularly in automated program repair (APR), they rely heavily on high-quality code repositories. Most code repositories are proprietary assets that capture the diversity and nuances of real-world industry software practices, which public datasets cannot fully represent. However, obtaining such data from various industries is hindered by data privacy concerns, as companies are reluctant to share their proprietary codebases. There has also been no in-depth investigation of collaborative software development by learning from private and decentralized data while preserving data privacy for program repair. To address the gap, we investigate federated learning as a privacy-preserving method for fine-tuning LLMs on proprietary and decentralized data to boost collaborative software development and maintenance. We use the private industrial dataset TutorCode for fine-tuning and the EvalRepair-Java benchmark for evaluation, and assess whether federated fine-tuning enhances program repair. We then further explore how code heterogeneity (i.e., variations in coding style, complexity, and embedding) and different federated learning algorithms affect bug fixing to provide practical implications for real-world software development collaboration. Our evaluation reveals that federated fine-tuning can significantly enhance program repair, achieving increases of up to 16.67% for Top@10 and 18.44% for Pass@10, even comparable to the bug-fixing capabilities of centralized learning. Moreover, the negligible impact of code heterogeneity implies that industries can effectively collaborate despite diverse data distributions. Different federated algorithms also demonstrate unique strengths across LLMs, suggesting that tailoring the optimization process to specific LLM characteristics can further improve program repair.

Software logs are essential records generated during the functioning of software systems, aiding in the identification of irregularities and prevention of system failures. Recently, deep learning models have garnered significant interest among researchers due to their efficacy in detecting anomalies within software logs. This research paper constructs a novel dataset, consisting of three parts: two datasets derived from our software system, along with a publicly available dataset obtained from the LogHub platform. The extensive logs within the dataset undergo preprocessing to extract meaningful features. Furthermore, this study introduces a novel model named SMAC-LSTM, designed specifically for detecting anomalies in software logs. Sequential Model-based Algorithm Configuration (SMAC) is a suitable method for hyperparameter optimization and automated deep learning. SMAC-LSTM involves determining the optimal hyperparameter values for the LSTM model using the SMAC. Additionally, SMAC-LSTM combines the temporal dependency capturing ability of Long Short-Term Memory (LSTM) with a context-dependent mechanism achieved through a Bayesian optimization algorithm based on random forests. This fusion enhances the model's ability to detect subtle anomalies in time series data, which are frequently disregarded by con-ventional LSTM models. The thorough evaluation demonstrates the superior performance of SMAC-LSTM models compared to traditional deep learning models, showcasing significant enhance-ments in precision (98.63%), and recall (92.31%), with an F1-Score of 95.36%, outperforming all other models. These results underscore the potential of SMAC-LSTM in the realm of software log anomaly detection.

Contrastive learning has emerged as a promising technique for hyperspectral image (HSI) classification. However, the inherent limitation of sliding window sampling in HSI results in partial samples within a mini-batch exhibiting extremely high similarity. Consequently, there is an increased number of negative sample pairs composed of similar samples, significantly reducing the effectiveness of contrastive learning. Moreover, prevailing classification models heavily depend on convolutional operations, emphasizing the extraction of local features but struggle to capture long-distance dependencies in both spatial and spectral dimensions. To address these problems and fully leverage the abundance of unlabeled samples, we propose a novel purified contrastive learning (PCL) framework for HSI classification. We design a complementary spatial-spectral representation encoder architecture that combines Convolutional Neural Network (CNN) and Transformer to capture local features and global dependencies. More importantly, a purified contrastive loss function is proposed based on super-pixel spatial prior. Extensive experiments on three public datasets demonstrate the superiority of PCL over state-of-the-art methods in HSI classification. The code for this work is available at https://github.com/zhaolin6/PCL for the sake of reproducibility.

Federated learning (FL) is an emerging machine learning setting designed to preserve privacy. However, constantly updating model parameters on uplink channels results in huge communication overload, which is a major challenge for FL. In this paper, we consider an adaptive gradient quantization approach based on rate-distortion optimization in FL, which consists of a non-stationary random walk model on the true global optimal model parameters. Unlike traditional quantization methods, our goal is to minimize the total communication costs when the global server reconstructs model parameters under distortion constraints. Furthermore, when considering the iterative process, we utilize the Kalman filter to reduce computational complexity. And in each iteration, a generalized water-filling algorithm is used to calculate the optimal quantization levels for each local client. Numerical results show that the proposed method outperforms conventional quantization methods in terms of reducing communication costs.

Hyperspectral image (HSI) classification is an active research topic in remote sensing. Supervised learning-based methods have been widely used in HSI classification tasks due to their powerful feature extraction capabilities for cases of sufficiently labeled samples. However, practical applications often have limited samples with accurate labels due to the high cost of labeling or unreliable visual interpretation. We introduce a contrastive self-supervised learning (SSL) algorithm to achieve HSI classification for problems with few labeled samples. First, a new HSI-specific augmentation module is developed to generate sample pairs. Then, a contrastive SSL model based on Siamese networks is used to extract features from these easily accessible sample pairs. Finally, the labeled samples are taken to fine-tune the parameters of the classification model to boost classification performance. Tests of the contrastive self-supervised algorithm have been performed on two widely used HSI datasets. The experimental results reveal that the proposed algorithm requires a few labeled samples to achieve superior performance.