Search Results for author:
Found 10 papers, 2 papers with code
Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion
The majority of human detection methods rely on the sensor using visible lights (e. g., RGB cameras) but such sensors are limited in scenarios with degraded vision conditions.
The Scope of In-Context Learning for the Extraction of Medical Temporal Constraints
Medications often impose temporal constraints on everyday patient activity.
ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence
It also addresses the challenges of accurately predicting RHBs central to MTCs (e. g., medication intake).
MiddleGAN: Generate Domain Agnostic Samples for Unsupervised Domain Adaptation
We extend the theory of GAN to show that there exist optimal solutions for the parameters of the two discriminators and one generator in MiddleGAN, and empirically show that the samples generated by the MiddleGAN are similar to both samples from the source domain and samples from the target domain.
An Intelligent Assistant for Converting City Requirements to Formal Specification
In this paper, we present CitySpec, an intelligent assistant system for requirement specification in smart cities.
E-ADDA: Unsupervised Adversarial Domain Adaptation Enhanced by a New Mahalanobis Distance Loss for Smart Computing
In the computer vision modality, the evaluation results suggest that we achieve new state-of-the-art performance on popular UDA benchmarks such as Office-31 and Office-Home, outperforming the second best-performing algorithms by up to 17. 9%.
Out-of-Distribution Detection
Unsupervised Domain Adaptation
STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks
In this paper, we develop a new temporal logic-based learning framework, STLnet, which guides the RNN learning process with auxiliary knowledge of model properties, and produces a more robust model for improved future predictions.
Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems
We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets.
Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
Inspired by the fact that most people carry smart wireless devices with them, e. g. smartphones, we propose to use this wireless identifier as a supervisory label.
