1

Unsupervised Machine Learning for Detecting and Locating Human-Made Objects in 3D Point Cloud

3D point clouds are unstructured, sparse, and irregular data collected by airborne LiDAR systems over a geological region. Laser pulses emitted from the systems reflect off objects both on and above the ground, resulting in data with the longitude, …

DenserNet: Weakly Supervised Visual Localization Using Multi-Scale Feature Aggregation

In this work, we introduce a Denser Feature Network (DenserNet) for visual localization. Our work provides three principal contributions. First, we develop a convolutional neural network (CNN) architecture which aggregates feature maps at different …

High-Order Orthogonal Decomposition for Tensors

Tensor decompositions are becoming increasingly important in processing images and videos. Previous methods, such as ANDECOMP/PARAFAC decomposition (CPD), Tucker decomposition (TKD), or tensor train decomposition (TTD), treat individual modes (or …

Low-Rank Sparse Tensor Approximations for Large High-Resolution Videos

Tensor decomposition techniques are becoming increasingly important in processing videos with large sizes and dimensions. Under the framework of CANDECOMP/PARAFAC decomposition (CPD), this work studies low-rank sparse tensor approximations (LRSTAs) …

PENet: Object Detection using Points Estimation High Definition Aerial Images

Aerial imagery has been increasingly adopted in mission-critical tasks, such as traffic surveillance, smart cities,and disaster assistance. However, identifying objects from aerial images faces the following challenges: 1) objects of …

Regression PCA for Moving Objects Separation

This work proposed a new approach called regression PCA (RegPCA) for statistical machine learning and big data analyses. One of the potential use cases investigated in this work is to separate the moving objects (foreground) from the background …

CHEESE: Cyber Human Ecosystem of Engaged Security Education

This Innovative Practice Full Paper presents CHEESE, a platform for cybersecurity education that complements formal classroom instruction with hands-on experience. With the ubiquitous use of computing devices and applications today, the protection of …

Sparse Block Regression (SBR) for Big Data with Categorical Variables

Categorical variables are nominal variables that classify observations by groups. The treatment of categorical variables in regression is a well-studied yet vital problem, with the most popular solution to perform a one hot encoding. However, …

Predicting Network Attacks with CNN by Constructing Images from NetFlow Data

Intrusion detection is a pivotal step for network protection. Usually, intrusion detection is performed at packet level by using deep packet or state-full protocol inspection to detect malicious requests in the network. However, flow based analyses …