Zobrazeno 1 - 10
of 556
pro vyhledávání: '"Xie Xiaofei"'
Large Language Model (LLM) is changing the software development paradigm and has gained huge attention from both academia and industry. Researchers and developers collaboratively explore how to leverage the powerful problem-solving ability of LLMs fo
Externí odkaz:
http://arxiv.org/abs/2411.01604
To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges to assess their overall performance on diverse vulnerability types. T
Externí odkaz:
http://arxiv.org/abs/2410.20740
GUI test migration aims to produce test cases with events and assertions to test specific functionalities of a target app. Existing migration approaches typically focus on the widget-mapping paradigm that maps widgets from source apps to target apps.
Externí odkaz:
http://arxiv.org/abs/2409.05028
As Generative Artificial Intelligence (GenAI) technologies evolve at an unprecedented rate, global governance approaches struggle to keep pace with the technology, highlighting a critical issue in the governance adaptation of significant challenges.
Externí odkaz:
http://arxiv.org/abs/2408.16771
Cyber-Physical Systems (CPSs) are increasingly prevalent across various industrial and daily-life domains, with applications ranging from robotic operations to autonomous driving. With recent advancements in artificial intelligence (AI), learning-bas
Externí odkaz:
http://arxiv.org/abs/2408.03892
Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable privacy-aware collaborative learning among AIoT devices. However, due
Externí odkaz:
http://arxiv.org/abs/2407.12729
Diffusion-based video generation has achieved significant progress, yet generating multiple actions that occur sequentially remains a formidable task. Directly generating a video with sequential actions can be extremely challenging due to the scarcit
Externí odkaz:
http://arxiv.org/abs/2405.18003
Deep Learning Systems (DLSs) have been widely applied in safety-critical tasks such as autopilot. However, when a perturbed input is fed into a DLS for inference, the DLS often has incorrect outputs (i.e., faults). DLS testing techniques (e.g., DeepX
Externí odkaz:
http://arxiv.org/abs/2405.09314
Autor:
Xia, Zeke, Hu, Ming, Yan, Dengke, Xie, Xiaofei, Li, Tianlin, Li, Anran, Zhou, Junlong, Chen, Mingsong
Federated Learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency and inference capability of FL is seriously limited due to the pres
Externí odkaz:
http://arxiv.org/abs/2404.12850
Although Split Federated Learning (SFL) is good at enabling knowledge sharing among resource-constrained clients, it suffers from the problem of low training accuracy due to the neglect of data heterogeneity and catastrophic forgetting. To address th
Externí odkaz:
http://arxiv.org/abs/2404.12846