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Found 9 papers, 2 papers with code
TPatch: A Triggered Physical Adversarial Patch
Evaluations with three object detectors, YOLO V3/V5 and Faster R-CNN, and eight image classifiers demonstrate the effectiveness of TPatch in both the simulation and the real world.
Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection
But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work.
CoughTrigger: Earbuds IMU Based Cough Detection Activator Using An Energy-efficient Sensitivity-prioritized Time Series Classifier
Persistent coughs are a major symptom of respiratory-related diseases.
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human--computer interaction, that measure and improve our daily lives.
A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection
Through experiments conducted on synthetic datasets and a real-world earbud-based cough dataset, we demonstrate the superiority of our proposed algorithm and present the result of cough detection with a single accelerometer sensor on the earbuds platform.
Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness
Recent research has found that navigation systems usually assume that all roads are equally safe, directing drivers to dangerous routes, which led to catastrophic consequences.
Human-Computer Interaction
NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions
Moreover, our system can achieve a F1-score of 77. 1% for episodes even in an all-day-around free-living setting.
Visualization of Multi-Objective Switched Reluctance Machine Optimization at Multiple Operating Conditions with t-SNE
However, the number of objectives that would need to be optimized would significantly increase with the number of operating points considered in the optimization, thus posting a potential problem in regards to the visualization techniques currently in use, such as in the scatter plots of Pareto fronts, the parallel coordinates, and in the principal component analysis (PCA), inhibiting their ability to provide machine designers with intuitive and informative visualizations of all of the design candidates and their ability to pick a few for further fine-tuning with performance verification.
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics -- A Comprehensive Review
In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications.
