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Found 13 papers, 7 papers with code
Real-Time Multimodal Cognitive Assistant for Emergency Medical Services
Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thinking and rapid decision-making.
Camera-Independent Single Image Depth Estimation from Defocus Blur
We created a synthetic dataset which can be used to test the camera independent performance of depth from defocus blur models.
KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems
Significant progress has been made in anomaly detection and run-time monitoring to improve the safety and security of cyber-physical systems (CPS).
DKEC: Domain Knowledge Enhanced Multi-Label Classification for Electronic Health Records
Multi-label text classification (MLTC) tasks in the medical domain often face long-tail label distribution, where rare classes have fewer training samples than frequent classes.
Robotic Scene Segmentation with Memory Network for Runtime Surgical Context Inference
However, runtime context inference is challenging since it requires timely and accurate detection of the interactions among the tools and objects in the surgical scene based on the segmentation of video data.
Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems
Adaptive Cruise Control (ACC) is a widely used driver assistance technology for maintaining the desired speed and safe distance to the leading vehicle.
Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates for Hyperglycemia Prevention
Significant advancements in type 1 diabetes treatment have been made in the development of state-of-the-art Artificial Pancreas Systems (APS).
Towards Surgical Context Inference and Translation to Gestures
We evaluate the performance of each stage of our method by comparing the results with the ground truth segmentation masks, the consensus context labels, and the gesture labels in the JIGSAWS dataset.
Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems
In this paper, we develop a safety assurance case for ML controllers in learning-enabled MCPS, with an emphasis on establishing confidence in the ML-based predictions.
Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems
The growing complexity of Cyber-Physical Systems (CPS) and challenges in ensuring safety and security have led to the increasing use of deep learning methods for accurate and scalable anomaly detection.
Runtime Detection of Executional Errors in Robot-Assisted Surgery
These Siamese networks also outperform gesture nonspecific task specific Siamese-CNN and Siamese-LSTM models for Suturing and Needle Passing.
Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems
Medical Cyber-physical Systems (MCPS) are vulnerable to accidental or malicious faults that can target their controllers and cause safety hazards and harm to patients.
On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products
Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives.
