As DNNs are embedded in more and more critical systems, it is essential to ensure that they
perform well on specific inputs. DNN repair has shown good results in fixing specific …

In AI-enabled CPSs, DNNs are used as controllers for the physical system. Despite their
advantages, DNN controllers can produce wrong control decisions, which can lead to safety …

Deep learning (DL) techniques have attracted much attention in recent years and have been
applied to many application scenarios. To improve the performance of DL models regarding …

With the increasing prevalence of software incorporating deep neural networks (DNNs),
quality assurance for these software systems has become a crucial concern. To this end …

Cyber-Physical Systems (CPSs) are increasingly adopting deep neural networks (DNNs) as
controllers, giving birth to AI-enabled CPSs. Despite their advantages, many concerns arise …

Rapid adoptions of Deep Learning (DL) in a broad range of fields led to the development of
specialised testing techniques for DL systems, including DL mutation testing. However …

DNN repair is an effective technique applied after training to enhance the class-specific
accuracy of classifier models, where a low failure rate is required on specific classes. The …

Deep Neural Networks (DNNs) suffer from significant retraining costs when adapting to
evolving requirements. Modularizing DNNs offers the promise of improving their reusability …

As an important kind of DNN (deep neural network), CNN (convolutional neural network)
has made remarkable progress and been widely used in the vision and decision-making of …

Deep Neural Networks (DNNs) are used for different tasks in many domains, some safety
critical like autonomous driving. When in operation, the DNN could misbehave on some …