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Found 20 papers, 7 papers with code
A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change
Coastal communities are at high risk of natural hazards due to unremitting global warming and sea level rise.
Deep Learning-based RF Fingerprint Authentication with Chaotic Antenna Arrays
Radio frequency (RF) fingerprinting is a tool which allows for authentication by utilizing distinct and random distortions in a received signal based on characteristics of the transmitter.
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems
We demonstrate how Wi-Fi deauthentication attack, a notoriously easy-to-perform and effective denial-of-service (DoS) attack, can be utilized to generate adversarial data for video anomaly detection systems.
End-to-End Semantic Video Transformer for Zero-Shot Action Recognition
While video action recognition has been an active area of research for several years, zero-shot action recognition has only recently started gaining traction.
TiSAT: Time Series Anomaly Transformer
While anomaly detection in time series has been an active area of research for several years, most recent approaches employ an inadequate evaluation criterion leading to an inflated F1 score.
Rethinking Video Anomaly Detection - A Continual Learning Approach
The experimental results show that the existing state-of-the-art methods are not suitable for the considered practical challenges, and the proposed algorithm outperforms them with a large margin in continual learning and few-shot learning tasks
Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative Models
The proposed algorithm is presented in detail for the commonly encountered heterogeneous datasets with real-valued (Gaussian) and categorical (multinomial) features.
An Efficient Approach for Anomaly Detection in Traffic Videos
We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic.
A Modular and Unified Framework for Detecting and Localizing Video Anomalies
Anomaly detection in videos has been attracting an increasing amount of attention.
Road Damage Detection using Deep Ensemble Learning
Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors.
Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities
We consider the network slicing problem of allocating the limited resources at the network edge (fog nodes) to vehicular and smart city users with heterogeneous latency and computing demands in dynamic environments.
Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate
Motivated by these research gaps, we propose an online anomaly detection method in surveillance videos with asymptotic bounds on the false alarm rate, which in turn provides a clear procedure for selecting a proper decision threshold that satisfies the desired false alarm rate.
Fast Unsupervised Anomaly Detection in Traffic Videos
In this paper, we propose a fast unsupervised anomaly detection system comprising of three modules: preprocessing module, candidate selection module and backtracking anomaly detection module.
Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks
Internet of Things (IoT) networks consist of sensors, actuators, mobile and wearable devices that can connect to the Internet.
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey
Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail.
Continual Learning for Anomaly Detection in Surveillance Videos
Anomaly detection in surveillance videos has been recently gaining attention.
Anomaly Detection In Surveillance Videos
Continual Learning
+2
Any-Shot Sequential Anomaly Detection in Surveillance Videos
Even though the performance of state-of-the-art methods on publicly available data sets has been competitive, they demand a massive amount of training data.
Ranked #18 on
Anomaly Detection
on CUHK Avenue
Online Multivariate Anomaly Detection and Localization for High-dimensional Settings
We further extend the proposed detection and localization methods to a supervised setup where an additional anomaly dataset is available, and combine the proposed semi-supervised and supervised algorithms to obtain an online learning algorithm under the semi-supervised framework.
Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings
In case the observed data have a low intrinsic dimensionality, we learn a submanifold in which the nominal data are embedded and evaluate whether the sequentially acquired data persistently deviate from the nominal submanifold.
Latent heterogeneous multilayer community detection
We propose a method for simultaneously detecting shared and unshared communities in heterogeneous multilayer weighted and undirected networks.
