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, various methods have been proposed to repair the misbehavior of DNNs by modifying ...

We proposed Precomputed Radiance Transfer of Gaussian Splats (PRTGS), a real-time high-quality relighting method for Gaussian splats in low-frequency lighting environments that captures soft shadows and interreflections by precomputing 3D Gaussian splats'...

Repairing deep neural networks (DNNs) to maintain its performance during deployment presents significant challenges due to the potential occurrence of unknown but common environmental corruptions. Most existing DNN repair methods only focus on repairing ...

The traditional 3D morphable model (3DMM) techniques generally regress model coefficients directly, neglecting the critical 2D spatial and semantic edge information. To address this limitation, we propose a multi-branch attention network (MBAN) ...

• GoogLeNet-AL introduces novel concepts for superior lung cancer detection.
• Mitigates biases with data augmentation and fairness-aware training.
• Achieves an accuracy of 98.74 % in lung cancer detection.
• Demonstrates a precision ...

As lung cancer has emerged as the top contributor to cancer-related fatalities, efficient and precise diagnostic methods are essential for efficient diagnosis. This research introduces a novel CNN architecture GoogLeNet with Adaptive Layers (...

Electron microscopy (EM) imaging offers unparalleled resolution for analyzing neural tissues, crucial for uncovering the intricacies of synaptic connections and neural processes fundamental to understanding behavioral mechanisms. Recently, the ...

Log anomaly detection, critical in identifying system failures and preempting security breaches, finds irregular patterns within large volumes of log data. Modern log anomaly detectors rely on training deep learning models on clean anomaly-free log data. ...

Point-cloud-based 3D object detection suffers from performance degradation when encountering data with novel domain gaps. To tackle it, single-domain generalization (SDG) aims to generalize the detection model trained in a limited single source ...

This article investigates the finite-time bipartite consensus problem of a class of nonlinear multi-agent systems (MASs) with communication link uncertainty under signed digraph. The existence of uncertainty affects the information exchange ...

Machine learning is routinely used to automate consequential decisions about users in domains such as finance and healthcare, raising concerns of transparency and recourse for negative outcomes. Existing Explainable AI techniques generate a static ...

The widespread adoption of video-based applications across various fields highlights their importance in modern software systems. However, in comparison to images or text, labelling video test cases for the purpose of assessing system accuracy can ...$$$$

Financial fraud in publicly traded companies involves the manipulation of accounting information through unethical practices, resulting in detrimental and deceitful actions that adversely affect the company's operations, economic progress, and societal ...

Brain tumors are highly lethal, representing 85–90% of all primary central nervous system (CNS) tumors. Magnetic resonance imaging (MRI) images are employed to identify and assess brain tumors. However, this process has historically relied heavily ...

Large Language Models (LLMs) have recently been widely used for code generation. Due to the complexity and opacity of LLMs, little is known about how these models generate code. We made the first attempt to bridge this knowledge gap by investigating ...

Over the past decade, Artificial Intelligence (AI) has had great success recently and is being used in a wide range of academic and industrial fields. More recently, Large Language Models (LLMs) have made rapid advancements that have propelled AI to a new ...

3D point cloud applications have become increasingly prevalent in diverse domains, showcasing their efficacy in various software systems. However, testing such applications presents unique challenges due to the high-dimensional nature of 3D point cloud ...

Vulnerability detectors based on deep learning (DL) models have proven their effectiveness in recent years. However, the shroud of opacity surrounding the decision-making process of these detectors makes it difficult for security analysts to comprehend. ...

Automated decision-making systems are increasingly deployed in domains such as hiring and credit approval where negative outcomes can have substantial ramifications for decision subjects. Thus, recent research has focused on providing explanations that ...

With the rapid recent development, deep learning based object detection techniques have been applied to various real-world software systems, especially in safety-critical applications like autonomous driving. However, few studies are conducted to ...

The recent advancements in Generative AI have significantly advanced the field of text-to-image generation. The state-of-the-art text-to-image model, Stable Diffusion, is now capable of synthesizing high-quality images with a strong sense of aesthetics. ...