Search Results for author:
Found 15 papers, 5 papers with code
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels
Human activity recognition (HAR) will be an essential function of various emerging applications.
Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach
We also share our experience in deploying COLA in our real-world cloud system, Cloud X.
Uncertainty-Encoded Multi-Modal Fusion for Robust Object Detection in Autonomous Driving
Multi-modal fusion has shown initial promising results for object detection of autonomous driving perception.
Interpersonal Distance Tracking with mmWave Radar and IMUs
This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances.
PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference
This paper presents the design of a system called PriMask, in which the mobile device uses a secret small-scale neural network called MaskNet to mask the data before transmission.
Indoor Smartphone SLAM with Learned Echoic Location Features
Indoor self-localization is a highly demanded system function for smartphones.
Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile Edge
This paper presents extensive evaluation of Sardino's performance in counteracting adversarial examples and applies it to build a real-time car-borne traffic sign recognition system.
On Lightweight Privacy-Preserving Collaborative Learning for Internet of Things by Independent Random Projections
This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator.
Origin-Aware Next Destination Recommendation with Personalized Preference Attention
Next destination recommendation is an important task in the transportation domain of taxi and ride-hailing services, where users are recommended with personalized destinations given their current origin location.
Kalibre: Knowledge-based Neural Surrogate Model Calibration for Data Center Digital Twins
However, in the context of CFD, each search step requires long-lasting CFD model's iterated solving, rendering these approaches impractical with increased model complexity.
Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference
Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks.
Challenges of Privacy-Preserving Machine Learning in IoT
The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence.
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data.
Moving Target Defense for Deep Visual Sensing against Adversarial Examples
Specifically, once the attackers obtain the deep model, they can construct adversarial examples to mislead the model to yield wrong classification results.
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator.
