Cross-project defect prediction (CPDP) uses the historical defect dataset collected from source projects to train a model and then applies it to the target project. However, almost all existing CPDP methods are developed for offline scenarios where the trained models are fixed and cannot be updated along with the incoming labeled target instances after training. Actually, the label of target instances usually arrives online in a streaming manner which can be used to update CPDP models for better defect prediction performance on the next unlabeled target instance. To bridge these gaps, we propose a novel effective online cross-project defect prediction framework named ALOGO. ALOGO includes two essential phases: offline cross-project defect prediction phase and online within-project defect prediction (WPDP) phase which are combined by an adaptive weighted adjustment mechanism. In the offline CPDP phase, the global offline defect knowledge is learned by minimizing the difference between the source and target datasets based on an offline CPDP model. In the online WPDP phase, the local online defect knowledge is learned based on an online WPDP model. These two kinds of defect knowledge are then combined to obtain the latest and most valuable defect knowledge. Experimental results on 27 defect datasets show that ALOGO improves the performance over the existing state-of-the-art online CPDP model by 31.2% in terms of the Matthews correlation coefficient (MCC) and also outperforms the baseline in terms of other four well-known measures. It can be concluded that 1) it is necessary to consider building online CPDP models; 2) ALOGO is a more promising alternative for online CPDP.

Iris recognition is widely regarded as one of the most reliable biometric identification technologies. Traditional methods, such as the Daugman algorithm typically normalize the annular iris region into a rectangular format during the preprocessing stage, followed by feature extraction and matching. However, these preprocessing steps often introduce distortions and struggle to adapt to multiresolution images, leading to inaccurate feature encoding. In response to these limitations, we propose a weak preprocessing algorithm for iris recognition that effectively preserves both grayscale and structural information of the iris. This approach is highly adaptable to varying image resolutions by leveraging a multiscale structural information extraction framework. It demonstrates significant improvements, achieving a matching accuracy of 96.67% on our proprietary dataset and 90% on the CASIA‐IrisV4 dataset. Compared to the Daugman and OsIris 4.0 algorithm using weak preprocessing schemes, our approach improves accuracy by 15.55% and reduces matching time by 16%. More importantly, this method presents a new idea that is different from traditional preprocessing methods with wider adaptability. It offers considerable potential for real‐world applications in security, with promising prospects for further integration with deep learning techniques.

To investigate the performance of different software defect number prediction (SDNP) models, we compared 22 SDNP models including six count models, five well-known single machine learning techniques, four ensemble learning techniques, four sampling techniques, and three hybrid techniques on 45 defect datasets in terms of fault-percentile-average (FPA), Popt, and root-mean-square-error (RMSE). The experimental results show that 1) count-model based SDNP models usually perform worst; 2) resampling, ensemble learning, and hybrid techniques are generally helpful for improving FPA and Popt; 3) SHSE outperforms other SDNP models in terms of FPA and Popt.

As people counting is becoming a research hotspot, a method for people counting based on multiple cameras information fusion is proposed. First, an adaptive sliding window algorithm is designed for people counting in single camera. Second, based on the homography theory, the common area between multiple cameras is calculated. Finally, the object matching strategy considering the factor of occlusion, is designed for improving the people counting results of single camera and achieving the overall people counting of multiple cameras. Experiments results have shown that, this method can perform well in complex scenes.