Search Results for author:
Found 31 papers, 5 papers with code
Exploring the Impacts of Land Use/Cover Change on Ecosystem Services in Multiple Scenarios --The Case of Sichuan-Chongqing Region, China
This study focuses on the "ecology-food" imbalance problem. taking Sichuan-Chongqing Region as an example, to set up future scenarios topredicate the distribution of ESs.
Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach
The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits, including enhanced efficiency, predictive capabilities, real-time responsiveness, and the enabling of autonomous operations.
LGL-BCI: A Lightweight Geometric Learning Framework for Motor Imagery-Based Brain-Computer Interfaces
The efficiency, assessed on two public EEG datasets and two real-world EEG devices, significantly outperforms the state-of-the-art solution in accuracy ($82. 54\%$ versus $62. 22\%$) with fewer parameters (64. 9M compared to 183. 7M).
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities
Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry.
Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System
As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios.
CUEING: a lightweight model to Capture hUman attEntion In driviNG
Discrepancies in decision-making between Autonomous Driving Systems (ADS) and human drivers underscore the need for intuitive human gaze predictors to bridge this gap, thereby improving user trust and experience.
iDML: Incentivized Decentralized Machine Learning
We leverage a smart contract not only for providing explicit incentives to end devices to participate in decentralized learning but also to create a fully decentralized mechanism to inspect and reflect on the behavior of the learning architecture.
A Physics-based and Data-driven Approach for Localized Statistical Channel Modeling
Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures.
Compressed CPD-Based Channel Estimation and Joint Beamforming for RIS-Assisted Millimeter Wave Communications
Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels.
PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition
Sleep stage recognition is crucial for assessing sleep and diagnosing chronic diseases.
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning
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.
Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM Systems: A Low-Rank Tensor Decomposition-Based Approach
We consider the problem of downlink channel estimation for intelligent reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems.
Temporal Convolution Domain Adaptation Learning for Crops Growth Prediction
We are the first to use the temporal convolution filters as the backbone to construct a domain adaptation network architecture which is suitable for deep learning regression models with very limited training data of the target domain.
Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems
In this paper, we propose a phase based Simultaneous Localization and Mapping (Phase-SLAM) framework for fast and accurate SLI sensor pose estimation and 3D object reconstruction.
GPS: A Policy-driven Sampling Approach for Graph Representation Learning
Graph representation learning has drawn increasing attention in recent years, especially for learning the low dimensional embedding at both node and graph level for classification and recommendations tasks.
Deep Open Set Identification for RF Devices
Artificial intelligence (AI) based device identification improves the security of the internet of things (IoT), and accelerates the authentication process.
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks
We present a spatial-temporal federated learning framework for graph neural networks, namely STFL.
OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaS
With the growing popularity of Android devices, Android malware is seriously threatening the safety of users.
Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous Vehicles
This technology enables drivers to use voice commands to control the vehicle and will be soon available in Advanced Driver Assistance Systems (ADAS).
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to counterpart almost any driving event, spanning from anti-fatigue safe driving to intelligent route planning.
Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications
We define a new approach, opportunistic federated learning, in which individual devices belonging to different users seek to learn robust models that are personalized to their user's own experiences.
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy.
DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling
To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic.
Leveraging AI and Intelligent Reflecting Surface for Energy-Efficient Communication in 6G IoT
The ever-increasing data traffic, various delay-sensitive services, and the massive deployment of energy-limited Internet of Things (IoT) devices have brought huge challenges to the current communication networks, motivating academia and industry to move to the sixth-generation (6G) network.
Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms
We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency.
Mitigating Sybil Attacks on Differential Privacy based Federated Learning
The state-of-the-art privacy-preserving technique in the context of federated learning is user-level differential privacy.
Cryptography and Security Distributed, Parallel, and Cluster Computing
Can Steering Wheel Detect Your Driving Fatigue?
In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel.
Progressive Defense Against Adversarial Attacks for Deep Learning as a Service in Internet of Things
To reduce the computational cost without loss of generality, we present a defense strategy called a progressive defense against adversarial attacks (PDAAA) for efficiently and effectively filtering out the adversarial pixel mutations, which could mislead the neural network towards erroneous outputs, without a-priori knowledge about the attack type.
ICS-Assist: Intelligent Customer Inquiry Resolution Recommendation in Online Customer Service for Large E-Commerce Businesses
Existing solution recommendation methods for online customer service are unable to determine the best solutions at runtime, leading to poor satisfaction of end customers.
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
We derive several implications for system and middleware builders: (1) when adding a defense component against adversarial attacks, it is important to deploy multiple defense methods in tandem to achieve a good coverage of various attacks, (2) a blackbox attack is much less effective compared with a white-box attack, implying that it is important to keep model details (e. g., model architecture, hyperparameters) confidential via model obfuscation, and (3) driving models with a complex architecture are preferred if computing resources permit as they are more resilient to adversarial attacks than simple models.
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data
Specifically, in this paper, we proposed a hybrid architecture which features a combination of Convolutional neural networks (CNN) and Long short-term Memory (LSTM) networks for HAR task.
