Search Results for author:
Found 9 papers, 2 papers with code
PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments
Our experiments on real-world Cyber-Physical Systems (CPS) and Internet of Things (IoT) network traffic datasets demonstrate that attackers can effectively backdoor a model by poisoning as little as 1\% or less of the entire training dataset.
BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos
This study presents a novel dataset from drone videos for baboon detection, tracking, and behavior recognition.
Streaming Video Analytics On The Edge With Asynchronous Cloud Support
In this work, we develop REACT, a framework that leverages cloud resources to execute large DNN models with higher accuracy to improve the accuracy of models running on edge devices.
sat2pc: Estimating Point Cloud of Building Roofs from 2D Satellite Images
In this paper, we propose sat2pc, a deep learning architecture that predicts the point cloud of a building roof from a single 2D satellite image.
Energy-Efficient Parking Analytics System using Deep Reinforcement Learning
Advances in deep vision techniques and ubiquity of smart cameras will drive the next generation of video analytics.
Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy
Internet of Things (IoT) devices are becoming increasingly popular and are influencing many application domains such as healthcare and transportation.
WattScale: A Data-driven Approach for Energy Efficiency Analytics of Buildings at Scale
In this paper, we present \texttt{WattScale}, a data-driven approach to identify the least energy-efficient buildings from a large population of buildings in a city or a region.
FastFabric: Scaling Hyperledger Fabric to 20,000 Transactions per Second
Blockchain technologies are expected to make a significant impact on a variety of industries.
Distributed, Parallel, and Cluster Computing
Making Contextual Decisions with Low Technical Debt
The Decision Service enables all aspects of contextual bandit learning using four system abstractions which connect together in a loop: explore (the decision space), log, learn, and deploy.
