Concurrent data structures or classes are designed to provide safe accesses and simultaneous updates by multiple threads to shared objects in a concurrent environment, with the goal of enhancing parallelism and throughput. However, testing concurrent objects poses significant challenges due to the potential explosion of concurrency test spaces, the variety of programming vulnerabilities, and the inherent nondeterminism of concurrent test executions. In this paper, we propose an Intrathread Method Orders based Adaptive Concurrency Testing (IMOACT) framework for concurrent objects. IMOACT can capture diverse behaviors of interthread method pairs through characterizing concurrent execution contexts with intrathread method orders. Moreover, IMOACT can adaptively optimize concurrent test executions by generating scheduling sequences based on the key scheduling points visited so far, streamlining test generation and execution organically across multiple tests. Experimental case studies with typical C/C++ concurrent classes demonstrate that IMOACT outperforms baseline approaches. On average, IMOACT promotes the effectiveness of detecting concurrency bugs by 65%, and achieves a speedup of 2.43x compared to the underlying state-of-the-art concurrency testing approach.

Lumbar disc degeneration, a progressive structural wear and tear of lumbar intervertebral disc, is regarded as an essential role on low back pain, a significant global health concern. Automated lumbar spine geometry reconstruction from MR images will enable fast measurement of medical parameters to evaluate the lumbar status, in order to determine a suitable treatment. Existing image segmentation-based techniques often generate erroneous segments or unstructured point clouds, unsuitable for medical parameter measurement. In this work, we present UNet-DeformSA and TransDeformer: novel attention-based deep neural networks that reconstruct the geometry of the lumbar spine with high spatial accuracy and mesh correspondence across patients, and we also present a variant of TransDeformer for error estimation. Specially, we devise new attention modules with a new attention formula, which integrate tokenized image features and tokenized shape features to predict the displacements of the points on a shape template. The deformed template reveals the lumbar spine geometry in an image. Experiment results show that our networks generate artifact-free geometry outputs, and the variant of TransDeformer can predict the errors of a reconstructed geometry. Our code is available at https://github.com/linchenq/TransDeformer-Mesh.

Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many research labs for automatic interpretation of ECG signals to identify potential abnormalities in patient hearts. Studies have shown that given a sufficiently large amount of data, the classification accuracy of DNNs could reach human-expert cardiologist level. However, despite of the excellent performance in classification accuracy, it has been shown that DNNs are highly vulnerable to adversarial noises which are subtle changes in input of a DNN and lead to a wrong class-label prediction with a high confidence. Thus, it is challenging and essential to improve robustness of DNNs against adversarial noises for ECG signal classification -a life-critical application. In this work, we designed a CNN for classification of 12-lead ECG signals with variable length, and we applied three defense methods to improve robustness of this CNN for this classification task. The ECG data in this study is very challenging because the sample size is limited, and the length of each ECG recording varies in a large range. The evaluation results show that our customized CNN reached satisfying F1 score and average accuracy, comparable to the top-6 entries in the CPSC2018 ECG classification challenge, and the defense methods enhanced robustness of our CNN against adversarial noises and white noises, with a minimal reduction in accuracy on clean data.