The vision transformer (ViT) model has the advantage of being able to model the long-range dependencies in the imagery and has been studied for the task of remote sensing image change detection (CD). However, the performance of the existing transformer-based CD methods is not satisfactory in the case of limited labeled data. The original self-attention mechanism cannot effectively extract the change information, and the large number of parameters in the ViT model makes the model difficult to train. To solve the above-mentioned problems, a semi-supervised pyramid cross-temporal attention transformer for change detection (CT2RCDSS) is proposed in this letter. The CT2RCDSS method follows an encoder-decoder structure. The encoder utilizes a dual-branch structure, containing the combination of the proposed cross-temporal attention (PCTA) and pyramid self-attention (PSA) mechanisms, which is designed to consider the interaction of the features from different time phases and enhance the changes at different scales. In the decoder, a series of deconvolutional layers with skip connections are utilized, and a Softmax layer follows to acquire the final binary change map. In addition, a semi-supervised training strategy, which reduces the errors in the pseudo-labels generated from the models initialized with different parameters, is used to improve the model stability while using unlabeled data. The experiments showed that the proposed method can achieve a superior F1-score and intersection over union (IoU), which indicates the potential of the proposed method.

File Edit Options Buffers Tools TeX Help Memory leaks have significant impact on software availability, performance, and security. Static analysis has been widely used to find memory leaks in C/C++ programs. Although a static analysis is able to find all potential leaks in a program, it often reports a great number of false warnings. Manually validating these warnings is a daunting task, which significantly limits the practicality of the analysis. In this paper, we develop a novel dynamic technique that automatically validates and categorizes such warnings to unleash the power of static memory leak detectors. Our technique analyzes each warning that contains information regarding the leaking allocation site and the leaking path, generates test cases to cover the leaking path, and tracks objects created by the leaking allocation site. Eventually, warnings are classified into four categories: MUST-LEAK, LIKELY-NOT-LEAK, BLOAT, and MAY-LEAK. Warnings in MUST-LEAK are guaranteed by our analysis to be true leaks. Warnings in LIKELY-NOT-LEAK are highly likely to be false warnings. Although we cannot provide any formal guarantee that they are not leaks, we have high confidence that this is the case. Warnings in BLOAT are also not likely to be leaks but they should be fixed to improve performance. Using our approach, the developer's manual validation effort needs to be focused only on warnings in the category MAY-LEAK, which is often much smaller than the original set.