Search Results for author: Yasin Yilmaz

Found 19 papers, 6 papers with code

A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change

no code implementations ICML 2020 Salman Sadiq Shuvo, Yasin Yilmaz, Alan Bush, Mark Hafen

Coastal communities are at high risk of natural hazards due to unremitting global warming and sea level rise.

Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems

no code implementations7 Apr 2022 Furkan Mumcu, Keval Doshi, Yasin Yilmaz

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.

Anomaly Detection BIG-bench Machine Learning +1

TiSAT: Time Series Anomaly Transformer

1 code implementation10 Mar 2022 Keval Doshi, Shatha Abudalou, Yasin Yilmaz

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.

Anomaly Detection Time Series

Zero-Shot Action Recognition with Transformer-based Video Semantic Embedding

no code implementations10 Mar 2022 Keval Doshi, Yasin Yilmaz

While video action recognition has been an active area of research for several years, zero-shot action recognition has only recently started gaining traction.

Action Recognition Zero-Shot Action Recognition

Rethinking Video Anomaly Detection - A Continual Learning Approach

no code implementations WACV 2022 Keval Doshi, Yasin Yilmaz

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

Anomaly Detection Continual Learning +1

Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative Models

1 code implementation27 Aug 2021 Yasin Yilmaz, Mehmet Aktukmak, Alfred O. Hero

The proposed algorithm is presented in detail for the commonly encountered heterogeneous datasets with real-valued (Gaussian) and categorical (multinomial) features.

Anomaly Detection Imputation +1

An Efficient Approach for Anomaly Detection in Traffic Videos

no code implementations20 Apr 2021 Keval Doshi, Yasin Yilmaz

We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic.

Anomaly Detection Change Detection

Road Damage Detection using Deep Ensemble Learning

1 code implementation30 Oct 2020 Keval Doshi, Yasin Yilmaz

Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors.

Ensemble Learning Management +1

Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities

no code implementations19 Oct 2020 Almuthanna Nassar, Yasin Yilmaz

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

1 code implementation10 Oct 2020 Keval Doshi, Yasin Yilmaz

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.

Anomaly Detection In Surveillance Videos Decision Making

Fast Unsupervised Anomaly Detection in Traffic Videos

1 code implementation CVPR 2020 Keval Doshi, Yasin Yilmaz

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.

Unsupervised Anomaly Detection

Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks

no code implementations15 Jun 2020 Keval Doshi, Yasin Yilmaz, Suleyman Uludag

Internet of Things (IoT) networks consist of sensors, actuators, mobile and wearable devices that can connect to the Internet.

Intrusion Detection

Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

no code implementations2 May 2020 Ammar Haydari, Yasin Yilmaz

Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail.

Autonomous Driving reinforcement-learning

Any-Shot Sequential Anomaly Detection in Surveillance Videos

no code implementations5 Apr 2020 Keval Doshi, Yasin Yilmaz

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.

Anomaly Detection In Surveillance Videos Decision Making +1

Online Multivariate Anomaly Detection and Localization for High-dimensional Settings

no code implementations17 May 2019 Mahsa Mozaffari, Yasin Yilmaz

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.

Anomaly Detection online learning

Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings

1 code implementation14 Sep 2018 Mehmet Necip Kurt, Yasin Yilmaz, Xiaodong Wang

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.

Anomaly Detection

Latent heterogeneous multilayer community detection

no code implementations16 Jun 2018 Hafiz Tiomoko Ali, Sijia Liu, Yasin Yilmaz, Romain Couillet, Indika Rajapakse, Alfred Hero

We propose a method for simultaneously detecting shared and unshared communities in heterogeneous multilayer weighted and undirected networks.

Community Detection

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