Evaluating the effectiveness of loop transformation optimizations requires benchmark suites that cover diverse loop properties and offer rich transformation opportunities. However, existing loop code datasets and generators face significant limitations in producing code with varied loop properties, particularly in generating realistic dependence patterns and complex loop structures. These limitations diminish their utility for comprehensive compiler testing and for training Large Language Models in code optimization. In this paper, we present PLCG, an enhanced parametric loop code generator that systematically produces diverse yet semantically legal loop code for optimization benchmarking. Building on parameter-driven approaches, PLCG introduces three key innovations: (1) a comprehensive parameter-to-property generation framework that translates high-level parameters into loop properties with array cross-statement coordination, diversified access functions, and flexible iteration domain; (2) a hybrid dependence generation strategy that combines parent-to-child and child-to-parent strategies to control dependence patterns while preventing illegal cycles; and (3) an adaptive parameter resolution module that employs weighted mechanisms for selecting dependence candidates, establishing iterator mappings, and assigning dependence distance values. We evaluate PLCG against state-of-the-art C loop code generators, including COLA-Gen, YARPGen, and LOOPRAG, across multiple dimensions: loop property diversity, loop transformation-triggering capability, and similarity to real-world code. Among twelve key loop properties, PLCG achieves five best-in-class metrics and six second-best metrics. In terms of transformation coverage, PLCG is the only generator that triggers all seven classical loop transformations, showing clear advantages in distribution and reversal, while delivering comparable results in the remaining transformations.

The analysis and evaluation of students’ online learning behaviors are crucial tasks in online education. Analyzing various behaviors during the learning process and obtaining the corresponding evaluation results can help teachers understand students’ learning conditions, adjust teaching strategies on time, and enhance the quality of teaching. However, the existing methods for evaluating learning behaviors are often based on one specific dimension and have difficulty analyzing student-learning behavior data simultaneously across multiple online platforms, leading to incomplete and inaccurate analysis results. In this paper, we propose a visual analysis pipeline based on a comprehensive scoring model of diverse features of online learning process data that is capable of detecting and analyzing learning behavior anomalies in various learning scenarios. We also developed a visual analysis system that demonstrates the effectiveness of our pipeline in multi-platform and multi-scenario learning behavior analysis. We illustrate the effectiveness and usability of the system through two usage scenarios and in-depth user interviews.

Kubernetes is a popular containerized application orchestration platform that is widely adopted for the development of large-scale service-oriented systems. Such a system often involves the integration of third-party applications, which may introduce security risks, such as excessive permission configurations and privilege escalations, resulting in the leakage of sensitive resources and unauthorized operations. Existing risk detection techniques mainly examine the permission configurations with some predefined rules, which may not be adaptive and precise due to the dynamic nature of Kubernetes environments. To overcome the limitations, we propose a systematic permission-oriented risk detection approach called KubeGuard. First, KubeGuard identifies a minimal permission set and uses it to check whether the existence of excessive permissions in the permission configurations. Second, KubeGuard detects various privilege escalation risks through dynamic rule-based auditing. Third, KubeGuard prevents sensitive resource leakages by monitoring the messages transferred between pods in Kubernetes and alerting in case sensitive resources are involved through the keyword matching. We further developed a supporting prototype called Kube-Guarder. Experiments were conducted on a suite of open-source Kubernetes applications and simulated scenarios to evaluate the effectiveness of KubeGuard. Experimental results have shown that KubeGuard can detect excessive permissions more effectively and precisely compared with the static rule-based baseline technique, and in the meanwhile, KubeGuard can detect various types of privilege escalation and sensitive resource leakage. As a result, this study delivered a promising technique for improving the security of Kubernetes applications.

Accurate neonatal brain MRI segmentation is valuable for investigating brain growth patterns and tracking the progression of neurodevelopmental disorders. However, it is a challenging task to use intensity-based methods to segment neonatal brain structures because of small contrast differences between brain regions caused by the inherent myelination process. Although convolutional neural networks offer the potential to segment brain structures in an intensity-independent manner, they suffer from lack of in-plane long-range dependency which is essential for the segmentation. To solve this problem, we propose a novel Transformer-Weighted network (TW-Net) to incorporate in-plane long-range dependency information. TW-Net employs a conventional encoder-decoder architecture with a Transformer module in the middle. The Transformer module uses a rotate-and-flip layer to better calculate the similarity between two patches in a slice to leverage similar patterns of geometrical and texture features within brain structures. In addition, a deep supervision module and squeeze-and-excitation blocks are introduced to incorporate boundary information of brain structures. Compared with state-of-the-art deep learning algorithms, TW-Net outperforms these methods for multiple-label tasks in 2D and 2.5D configurations on two independent public datasets, demonstrating that TW-Net is a promising method for neonatal brain MRI segmentation.

Segmenting the fine structure of the mouse brain on magnetic resonance (MR) images is critical for delineating morphological regions, analyzing brain function, and understanding their relationships. Compared to a single MRI modality, multimodal MRI data provide complementary tissue features that can be exploited by deep learning models, resulting in better segmentation results. However, multimodal mouse brain MRI data is often lacking, making automatic segmentation of mouse brain fine structure a very challenging task. To address this issue, it is necessary to fuse multimodal MRI data to produce distinguished contrasts in different brain structures. Hence, we propose a novel disentangled and contrastive GAN-based framework, named MouseGAN++, to synthesize multiple MR modalities from single ones in a structure-preserving manner, thus improving the segmentation performance by imputing missing modalities and multi-modality fusion. Our results demonstrate that the translation performance of our method outperforms the state-of-the-art methods. Using the subsequently learned modality-invariant information as well as the modality-translated images, MouseGAN++ can segment fine brain structures with averaged dice coefficients of 90.0% (T2w) and 87.9% (T1w), respectively, achieving around +10% performance improvement compared to the state-of-the-art algorithms. Our results demonstrate that MouseGAN++, as a simultaneous image synthesis and segmentation method, can be used to fuse cross-modality information in an unpaired manner and yield more robust performance in the absence of multimodal data. We release our method as a mouse brain structural segmentation tool for free academic usage at https://github.com/yu02019.