14 citations · 27 across the 9 of their papers we have counts for
9 papers
ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of Life
Chandrakanth Gudavalli, Bowen Zhang, Connor Levenson +2
In this paper, we present ReeFRAME, a scalable Reeb graph-based framework designed to analyze vast volumes of GPS-enabled human trajectory data generated at 1Hz frequency. ReeFRAME…
Implicit Context-aware Learning and Discovery for Streaming Data Analytics
Kin Gwn Lore, Kishore K. Reddy
The performance of machine learning model can be further improved if contextual cues are provided as input along with base features that are directly related to an inference task.…
Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems
Chao Liu, Kin Gwn Lore, Zhanhong Jiang +1
Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, di…
Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection
Devu Manikantan Shilay, Kin Gwn Lorey, Tianshu Weiz +2
A significant challenge in energy system cyber security is the current inability to detect cyber-physical attacks targeting and originating from distributed grid-edge devices such…
Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
Aditya Balu, Sambit Ghadai, Kin Gwn Lore +3
3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. In this paper, we present a 3D-CNN based method to learn…
Data-driven root-cause analysis for distributed system anomalies
Chao Liu, Kin Gwn Lore, Soumik Sarkar
Modern distributed cyber-physical systems encounter a large variety of anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong co…
Deep Action Sequence Learning for Causal Shape Transformation
Kin Gwn Lore, Daniel Stoecklein, Michael Davies +2
Deep learning became the method of choice in recent year for solving a wide variety of predictive analytics tasks. For sequence prediction, recurrent neural networks (RNN) are ofte…
Deep Value of Information Estimators for Collaborative Human-Machine Information Gathering
Kin Gwn Lore, Nicholas Sweet, Kundan Kumar +2
Effective human-machine collaboration can significantly improve many learning and planning strategies for information gathering via fusion of 'hard' and 'soft' data originating fro…
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
Kin Gwn Lore, Adedotun Akintayo, Soumik Sarkar
In surveillance, monitoring and tactical reconnaissance, gathering the right visual information from a dynamic environment and accurately processing such data are essential ingredi…