Engineering Commons^™

Person Identification With Convolutional Neural Networks, Kang Zheng

Theses and Dissertations

Person identification aims at matching persons across images or videos captured by different cameras, without requiring the presence of persons’ faces. It is an important problem in computer vision community and has many important real-world applica- tions, such as person search, security surveillance, and no-checkout stores. However, this problem is very challenging due to various factors, such as illumination varia- tion, view changes, human pose deformation, and occlusion. Traditional approaches generally focus on hand-crafting features and/or learning distance metrics for match- ing to tackle these challenges. With Convolutional Neural Networks (CNNs), feature extraction and metric learning can be combined in …

Cybersecurity Issues In The Context Of Cryptographic Shuffling Algorithms And Concept Drift: Challenges And Solutions, Hatim Alsuwat

Theses and Dissertations

In this dissertation, we investigate and address two kinds of data integrity threats. We first study the limitations of secure cryptographic shuffling algorithms regarding preservation of data dependencies. We then study the limitations of machine learning models regarding concept drift detection. We propose solutions to address these threats.

Shuffling Algorithms have been used to protect the confidentiality of sensitive data. However, these algorithms may not preserve data dependencies, such as functional de- pendencies and data-driven associations. We present two solutions for addressing these shortcomings: (1) Functional dependencies preserving shuffle, and (2) Data-driven asso- ciations preserving shuffle. For preserving functional dependencies, …

Properties, Learning Algorithms, And Applications Of Chain Graphs And Bayesian Hypergraphs, Mohammad Ali Javidian

Theses and Dissertations

Probabilistic graphical models (PGMs) use graphs, either undirected, directed, or mixed, to represent possible dependencies among the variables of a multivariate probability distri- bution. PGMs, such as Bayesian networks and Markov networks, are now widely accepted as a powerful and mature framework for reasoning and decision making under uncertainty in knowledge-based systems. With the increase of their popularity, the range of graphical models being investigated and used has also expanded. Several types of graphs with dif- ferent conditional independence interpretations - also known as Markov properties - have been proposed and used in graphical models.

The graphical structure of a …

Stacked Modelling Framework, Kareem Abdelfatah

Theses and Dissertations

The thesis develops a predictive modeling framework based on stacked Gaussian processes and applies it to two main applications in environmental and chemical en- gineering. First, a network of independently trained Gaussian processes (StackedGP) is introduced to obtain analytical predictions of quantities of interest (model out- puts) with quantified uncertainties. StackedGP framework supports component- based modeling in different fields such as environmental and chemical science, en- hances predictions of quantities of interest through a cascade of intermediate predic- tions usually addressed by cokriging, and propagates uncertainties through emulated dynamical systems driven by uncertain forcing variables. By using analytical first and …

Challenges In Large-Scale Machine Learning Systems: Security And Correctness, Emad Alsuwat

Theses and Dissertations

In this research, we address the impact of data integrity on machine learning algorithms. We study how an adversary could corrupt Bayesian network structure learning algorithms by inserting contaminated data items. We investigate the resilience of two commonly used Bayesian network structure learning algorithms, namely the PC and LCD algorithms, against data poisoning attacks that aim to corrupt the learned Bayesian network model.

Data poisoning attacks are one of the most important emerging security threats against machine learning systems. These attacks aim to corrupt machine learning models by con- taminating datasets in the training phase. The lack of resilience of …