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Found 16 papers, 4 papers with code
EnCoMP: Enhanced Covert Maneuver Planning using Offline Reinforcement Learning
Cover navigation in complex environments is a critical challenge for autonomous robots, requiring the identification and utilization of environmental cover while maintaining efficient navigation.
TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments
Additionally, TopoNav incorporates intrinsic motivation to guide exploration toward relevant regions and frontier nodes in the topological map, addressing the challenges of sparse extrinsic rewards.
Enhancing Robotic Navigation: An Evaluation of Single and Multi-Objective Reinforcement Learning Strategies
This study presents a comparative analysis between single-objective and multi-objective reinforcement learning methods for training a robot to navigate effectively to an end goal while efficiently avoiding obstacles.
CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning
We evaluate CoverNav using the Unity simulation environment and show that it guarantees dynamically feasible velocities in the terrain when fed with an elevation map generated by another DRL based navigation algorithm.
Novel Categories Discovery Via Constraints on Empirical Prediction Statistics
As an alternative to the traditional pseudo-labeling-based approaches, we leverage the connection between the data sampling and the provided multinoulli (categorical) distribution of novel classes.
HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous Systems
Finally, we propose and optimize a novel parameter split-ratio, which indicates the proportion of the data required to be offloaded to another device while considering the networking bandwidth, busy factor, memory (CPU, GPU, RAM), and power constraints of the devices in the testbed.
A Systematic Study on Object Recognition Using Millimeter-wave Radar
Image and video data are light and weather-dependent, susceptible to the occlusion effect, and present privacy problems.
NEV-NCD: Negative Learning, Entropy, and Variance regularization based novel action categories discovery
During our experiments with UCF101 and multi-view action dataset, NEV-NCD achieves ~ 83% classification accuracy in test instances of labeled data.
Recent Advancements in Machine Learning For Cybercrime Prediction
We start the review with some standard methods cybercriminals use and then focus on the latest machine and deep learning techniques, which detect anomalous behavior and identify potential threats.
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey
Machine learning-based wearable human activity recognition (WHAR) models enable the development of various smart and connected community applications such as sleep pattern monitoring, medication reminders, cognitive health assessment, sports analytics, etc.
Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions
The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities.
Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning
We empirically proved the validity of our method with three benchmark datasets and found a minimal accuracy drop in the global model after applying quantization.
Demo: RhythmEdge: Enabling Contactless Heart Rate Estimation on the Edge
In this demo paper, we design and prototype RhythmEdge, a low-cost, deep-learning-based contact-less system for regular HR monitoring applications.
AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment
Cognitive impairment has become epidemic in older adult population.
Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning
We investigate the problem of localized flood detection using the social sensing model (Twitter) in order to provide an efficient, reliable and accurate flood text classification model with minimal labeled data.
Estimating Buildings' Parameters over Time Including Prior Knowledge
We perform extensive evaluations on two datasets to understand the generative process and show that the Bayesian approach is more interpretable.
