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High Performance Algorithms for Exact Structure Learning of Bayesian Networks
(State University of New York at Buffalo, 2019)
Bayesian networks (BNs) are probabilistic graphical models often used in big data analytics to capture conditional relationships among a set of random variables. They are critical class of models that are capable to represent ...
Examining the Nature of Collaborative Learning in the Context of Problem Solving in First-Year and Second-Year Computing Education, A Naturalistic Exploratory Case Study
(State University of New York at Buffalo, 2019)
This exploratory case study examined the nature of problem solving in terms of the types of problems posed, instructional approaches and collaborative learning, in first-year and second-year post-secondary computing ...
Moral Psychology: An Ontological Approach
(State University of New York at Buffalo, 2019)
In information science, an ontology is a controlled vocabulary that provides names and definitions for the classes and relations among entities within a realm of discourse, and which can then be used to make interoperable ...
Hardware-rooted Device Authentication for Smart Devices
(State University of New York at Buffalo, 2019)
Smart devices play an increasingly important role in our daily life. Unintentional faults and malicious attacks could bring great danger to human lives and the environment, especially in safety-critical applications like ...
TOWARDS DEPENDABLE NETWORK FUNCTION VIRTUALIZATION SERVICES
(State University of New York at Buffalo, 2019)
Despite the great momentum NFV has gained over the last few years, a closer look under the hood reveals that NFV introduces numerous dependability challenges. Compared to traditional IT applications with the availability ...
Efficient and Scalable Metadata Access for Distributed Applications from Edge to the Cloud
(State University of New York at Buffalo, 2019)
We are witnessing a new era that offers new opportunities to conduct data-intensive scientific research with the help of recent advancements in computational, storage, and network technologies. With the rapid deployment ...
Representation Learning and Data Augmentation in Forensic Comparison
(State University of New York at Buffalo, 2018)
The forensic comparison problem is about determining whether observed evidence arises from a known source by comparing the evidence with known samples side by side. The opinion of a forensic comparison system is characterized ...
ALGORITHMS FOR AUTOMATIC OBJECT DETECTION AND LANDMARK DETECTION IN MEDICAL IMAGES
(State University of New York at Buffalo, 2018)
Object detection and landmark detection are two basic research problems in many computer vision applications. In this dissertation we research various aspects of above problems and demonstrate our work through two applications: ...
Android Security via Static Analysis Techniques
(State University of New York at Buffalo, 2018)
Android is a popular platform designed for mobile devices. It consists of a customized Linux kernel, middleware, and a few core applications such as the Phone application. The middleware, commonly referred to as the Android ...
New Computational Geometry Methods for Some Fundamental Machine Learning Problems
(State University of New York at Buffalo, 2018)
Machine learning concerns the construction of techniques that can learn from and make predictions on data. The computational geometry can play a crucial and natural role in machine learning when exploring the data structure. ...