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Automated test case generation based on path coverage is not only a large-scale black-box optimization problem, but also a key scientific problem in software automatic testing technology. Evolutionary algorithms and other search-based algorithms ...

• The ATCG-PC is modeled as a multi-task optimization problem.
• An EA is designed to solve ATCG-PC based on manifold optimization.
• Experimental results show the outperformance of our method on ATCG-PC.

Smart contracts are programs that reside on a block-chain. A key feature of smart contracts is their immutability, meaning that they cannot be modified once they are deployed. Despite existing efforts to uncover vulnerabilities, a common assumption is ...

Cracks are among the most common, most likely, and earliest of all pavement distresses. Detecting and repairing cracks as early as possible can help extend the service life of pavements. However, Detecting cracks with precision can be challenging due to ...

We present Large Language Model for Mixed Reality (LLMR), a framework for the real-time creation and modification of interactive Mixed Reality experiences using LLMs. LLMR leverages novel strategies to tackle difficult cases where ideal training data is ...

Automated test data generation based on path coverage (ATDG-PC) is an essential task in software testing. However, existing concolic testing approaches typically employ single static analyses or search-based algorithms for different paths without ...

Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we ...

Recently, neural implicit functions have demonstrated remarkable results in the field of multi-view reconstruction. However, most existing methods are tailored for dense views and exhibit unsatisfactory performance when dealing with sparse views. Several ...

Direct numerical simulation (DNS) is a technique that directly solves the fluid Navier-Stokes equations with high spatial and temporal resolutions, which has driven much research regarding the nature of turbulence. For high-Reynolds number (Re) ...

Automatic classifier accuracy evaluation (ACAEval) on unlabeled test sets is critical for unseen real-world environments. The use of dataset-level regression on synthesized meta-datasets (comprised of many sample sets) has shown promising results ...

• Two authors named Yan Huang in pinyin, one PostDoc (1st author), the other an Associate Prof (3rd author).
• Paper on ACAEval for classifier accuracy. Uses meta-dataset technique for unlabeled real-world data.
• Customized meta-dataset ...

With the development and progress of society, Chinese people have higher and higher requirements for the quality of life. The rapid development of urbanization and industrialization has led to a large number of environmental pollution problems, ...

Consensus representation learning is one of the most popular approaches in the field of multi-view clustering. However, most of the existing methods cannot learn discriminative representations with a clustering-friendly structure since these methods ...

A Software Product Line (SPL) is a set of software products that are built from a variability model. Real-world SPLs typically involve a vast number of valid products, making it impossible to individually test each of them. This arises the need for ...

Early diagnosis of renal cancer can greatly improve the survival rate of patients. Contrast-enhanced ultrasound (CEUS) is a cost-effective and non-invasive imaging technique and has become more and more frequently used for renal tumor diagnosis. ...

Mean-field games (MFGs) are a modeling framework for systems with a large number of interacting agents. They have applications in economics, finance, and game theory. Normalizing flows (NFs) are a family of deep generative models that ...

Constrained optimization is an active research topic in evolutionary computation. It challenges the evolutionary algorithms (EAs) in allocating fitness evaluations to the minimization of constraint violations and the optimization of objectives. Most ...

Image matting is an important process in digital image processing, with pixel pair optimization-based methods having distinct advantages in parallelization and handling mislabeled trimaps or spatially disconnected foregrounds. Nevertheless, such ...

• The pixel pair optimization problem can be solved effectively by constructing and optimizing a surrogate model.
• Image matting algorithm based on surrogate model can provide high-quality alpha mattes with 1% of the pixel pair evaluation ...

Existing approaches to animatable NeRF-based head avatars are either built upon face templates or use the expression coefficients of templates as the driving signal. Despite the promising progress, their performances are heavily bound by the expression ...

Untethered miniature robots that can access narrow and harsh environments in the body show great potential for future biomedical applications. Despite the many types of millirobot that have been developed, swimming against the fast blood flow remains a ...

Single Image Super-Resolution (SISR) tasks have achieved significant performance with deep neural networks. However, the large number of parameters in CNN-based methods for SISR tasks require heavy computations. Although several efficient SISR models have ...

The unsupervised image-to-image translation aims to learn a mapping that translates images from one domain to the target domain. Current state-of-the-art generative adversarial network (GAN) models utilize time and space-costly operators to ...