Computer science and systems (Tacoma)
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Hardening DGA Classifiers Using Adversarial Attacks and IVAP
Domain generation algorithms (DGAs) are utilized by botmasters as a way to connect malware-infected machines with the botmaster's command-and-control center (C\&C). Such a connection allows the botmaster to send and receive ... -
Street Parking Sign Detection, Recognition and Trust System
Parking is one of the major problems in autonomous driving. Although cars can park in a parking spot automatically now, they can't find where they can park. In this thesis, we propose a novel street parking sign detection ... -
Authority, Expertise, and Active Learning in the CS Classroom
Active learning is a teaching practice that involves students in the learning process as more than mere passive listeners, and there is ample evidence of its benefits. Learning is placed more in the hands of the students ... -
Hardening Inline DGA Classifiers Against Adversarial Attacks
Domain Generation Algorithms (DGAs) are widely used by cybercriminals to generate domain names on-the-go for C&C (command-and-control) purposes of establishing communication with the bots and instructing them to perform ... -
Privacy-Preserving Machine Learning Applications
Machine learning has its many applications in different areas of interest that involves huge amounts of data in order to learn how to recognize, predict, and classify. One such area is in text classification where a private ... -
Predicting German Compound Words Using a Recurrent Neural Network
Accurate classification, morphological analysis and translation of compound words is a problem that has not been satisfactorily solved in many of its aspects. For example, as of the date of this paper, Google translates ... -
Toward an Accurate Acoustic Localization System
In this dissertation, we propose an accurate and fast multi-pair simultaneous localization systems for smartphones without the need of infrastructure support. The system is a purely software-based solution (an App), which ... -
Investigation of Extending Feature Selection Algorithms to Explicit Feature Selection in Kernel Space
Feature selection methods play important roles in the area of machine learning. Being a part of prepossessing, the technology of feature selection can select useful information from raw data. A good feature selection method ... -
Reconfigurable Convolution Implementation for CNNs in FPGAs
Deep learning continues to be the revolutionary method used in pattern recognition applications including image, video, and speech processing. Convolutional Neural Networks (CNNs) in particular have outperformed every ... -
Evaluating the fairness in the performance of machine learning methods
Machine learning plays an increasingly important role in our lives, tackling both prevalent and specialized but high-risk problems. Motivated by legislation, responsibility to ensure transparency and accountability of ... -
SMPCEngine: An N-Party Implementation of the Secure Multiparty Private Computation Protocol
In this thesis, we implement a framework for secure multiparty computation. Our framework works in the commodity-based model, where the players running the distributed computation receive pre-distributed data from a trusted ... -
Automatic Detection of Providers with Excess Healthcare Spending
This thesis aims to develop techniques to help large hospital systems to detect providers with excess spending. Identifying fraud, waste, and abuse resulting in super uous expendi- tures associated with care delivery is ... -
Performance Analysis of Real Time Streaming Systems for Smart Buildings
The Internet of Things (IoT) extends traditional cyber-physical systems by linking sensor-based edge devices to additional network-accessible services and resources. In most current IoT deployments, sensor data is streamed ... -
Integrating external biological knowledge in the construction of regulatory networks from LINCS data
The inference of gene regulatory networks is of great interest and has various applications. The recent advances in high-throughout biological data collection have facilitated the construction and understanding of gene ... -
A Sequence Based Approach for Predicting Clinical Events
The data associated to each patient increases almost linearly as the patient flows through the continuum of care. Analysis of the data collected during a patient’s admission to the hospital reveals that it grows vertically ... -
Communication and Round Balanced Oblivious FSM Evaluation
Privacy is a major issue in the age of the Internet. Many advances are being made in Cryptography regarding performing computations over private data, both in homomorphic encryption, multi-party computation, and applications ... -
Scalable Propagation Algorithms for Node Labeling in Bipartite Graphs
Bipartite graphs are graphs whose vertices can be partitioned into 2 different independent sets. Many interesting machine learning problems either present themselves naturally as problems of node ... -
Predictive Analytics and Decision Support for Heart Failure patients
In the last few years, legislations such as the Patient Protection and A ordable Care Act, also known as Obamacare, have emphasised the need for improving the quality of health care. Part of the programs introduced by this ... -
Classifying Child Maltreatment by Brain Imaging Data
Childhood maltreatment is a condition that leads to the development of behavioral issues and affect brain structure and functionality. Recent neuroimaging studies provide connection between deficits in brain volume, gray ... -
Application of Machine Learning Techniques to Acute Myeloid Leukemia
This thesis is inspired by the position paper ”Predictive, personalized, preventive, par- ticipatory (P4) cancer medicine” [1]. The basic concept of P4 medicine was “The right patient with the right drug at the right dose ...
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