19 citations · 62 across the 19 of their papers we have counts for
19 papers
NEUROSYMLAND: Neuro-Symbolic Landing-Site Assessment for Robust and Edge-Deployable UAV Autonomy
Weixian Qian, Tianyi Yang, Sebastian Schroder +5
Safe landing-site assessment in unstructured environments remains a key challenge for autonomous UAV deployment, as vision-only learning approaches often degrade under terrain vari…
From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing
Linfeng Liang, Xiao Cheng, Tsong Yueh Chen +1
Simulation-based testing of autonomous driving systems (ADS) must uncover realistic and diverse failures in dense, heterogeneous traffic. However, existing search-based seeding met…
Distributed Retrieval-Augmented Generation
Chenhao Xu, Longxiang Gao, Yuan Miao +1
As large language models (LLMs) become increasingly adopted on edge devices, Retrieval-Augmented Generation (RAG) is gaining prominence as a solution to address factual deficiencie…
LGL-BCI: A Motor-Imagery-Based Brain-Computer Interface with Geometric Learning
Jianchao Lu, Yuzhe Tian, Yang Zhang +2
Brain--computer interfaces are groundbreaking technology whereby brain signals are used to control external devices. Despite some advances in recent years, electroencephalogram (EE…
NeuroStrata: Harnessing Neurosymbolic Paradigms for Improved Design, Testability, and Verifiability of Autonomous CPS
Xi Zheng, Ziyang Li, Ivan Ruchkin +2
Autonomous cyber-physical systems (CPSs) leverage AI for perception, planning, and control but face trust and safety certification challenges due to inherent uncertainties. The neu…
Grey-Box Fuzzing in Constrained Ultra-Large Systems: Lessons for SE Community
Jiazhao Yu, Yanlun Tu, Zhanlei Zhang +5
Testing ultra-large microservices-based FinTech systems presents significant challenges, including restricted access to production environments, complex dependencies, and stringent…
Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach
Xi Zheng, Aloysius K. Mok, Ruzica Piskac +5
The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits, including enhanced efficiency, predictive capabilities, real-time responsive…
LGL-BCI: A Motor-Imagery-Based Brain-Computer Interface with Geometric Learning
Jianchao Lu, Yuzhe Tian, Yang Zhang +2
Brain--computer interfaces are groundbreaking technology whereby brain signals are used to control external devices. Despite some advances in recent years, electroencephalogram (EE…
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities
Tiehua Zhang, Rui Xu, Jianping Zhang +4
Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry. Traditionally, software security is safeguarded b…
Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System
Jiwei Guan, Lei Pan, Chen Wang +3
There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become avai…
TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction
Yao Deng, Jiaohong Yao, Zhi Tu +3
Recent incidents with autonomous vehicles highlight the need for rigorous testing to ensure safety and robustness. Constructing test scenarios for autonomous driving systems (ADSs)…
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning
Tiehua Zhang, Yuze Liu, Zhishu Shen +4
Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medi…
OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaS
Guangquan Xu, GuoHua Xin, Litao Jiao +4
With the growing popularity of Android devices, Android malware is seriously threatening the safety of users. Although such threats can be detected by deep learning as a service (D…
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
Yao Deng, Tiehua Zhang, Guannan Lou +3
The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to coun…
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Chenhao Xu, Jiaqi Ge, Yong Li +5
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy. FL is effective when dealing with independent and identical…
DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling
Shangming Cai, Dongsheng Wang, Haixia Wang +4
To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared mod…
A Declarative Metamorphic Testing Framework for Autonomous Driving
Yao Deng, Xi Zheng, Tianyi Zhang +4
Autonomous driving has gained much attention from both industry and academia. Currently, Deep Neural Networks (DNNs) are widely used for perception and control in autonomous drivin…
Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms
Haimiao Mo, Shuai Ding, Shanlin Yang +2
Monitoring the respiratory rate is crucial for helping us identify respiratory disorders. Devices for conventional respiratory monitoring are inconvenient and scarcely available. R…
Mitigating Sybil Attacks on Differential Privacy based Federated Learning
Yupeng Jiang, Yong Li, Yipeng Zhou +1
In federated learning, machine learning and deep learning models are trained globally on distributed devices. The state-of-the-art privacy-preserving technique in the context of fe…