Thesis (Ph. D.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 125-128). Also available on the World Wide Web.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 107-113).

Thesis (Ph. D.)--State University of New York at Buffalo, 1998. / "April 1998." Includes bibliographical references (leaves 144-153). Also available in print.

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 381-406).

No description available.

Thesis (M.S.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (Ph. D.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 93-98).

Cardiac arrhythmias occur when the normal pattern of electrical signals in the heart breaks down. A premature ventricular contraction (PVC) is a common type of arrhythmia that occurs when a heartbeat originates from an ectopic focus within the ventricles rather than from the sinus node in the right atrium. This and other arrhythmias are often diagnosed with the help of an electrocardiogram, or ECG, which records the electrical activity of the heart using electrodes placed on the skin. In an ECG signal, a PVC is characterized by both timing and morphological differences from a normal sinus beat. An implantable cardiac monitor (ICM) is a device used to help physicians diagnose and monitor infrequent cardiac arrhythmias that may not be observed during an ECG recording performed during a normal clinic visit. These devices are implanted under the skin of the chest and simply monitor and record the electrical activity of the heart. The recorded signal is referred to as a subcutaneous electrocardiogram, or SECG. This thesis proposes and tests a novel algorithm that uses an SECG signal to perform PVC detection and is suitable for implementation within an implantable cardiac monitoring device. The proposed algorithm uses a combination of morphological and timing criteria to identify PVCs in near real time. Current commercially-available ICMs do not provide a PVC detection feature, so the proposed algorithm could help provide physicians with valuable additional diagnostic information about a clinically-significant arrhythmia.

Computational risk detection has the potential to protect especially vulnerable populations from online victimization. Conducting a comprehensive literature review on computational approaches for online sexual risk detection led to the identification that the majority of this work has focused on identifying sexual predators after-the-fact. Also, many studies rely on public datasets and third-party annotators to establish ground truth and train their algorithms, which do not accurately represent young social media users and their perspectives to prevent victimization. To address these gaps, this dissertation integrated human-centered approaches to both creating representative datasets and developing sexual risk detection machine learning models to ensure the broader societal impacts of this important work. In order to understand what and how adolescents talk about their online sexual interactions to inform study designs, a thematic content analysis of posts by adolescents on an online peer support mental health was conducted. Then, a user study and web-based platform, Instagram Data Donation (IGDD), was designed to create an ecologically valid dataset. Youth could donate and annotate their Instagram data for online risks. After participating in the study, an interview study was conducted to understand how youth felt annotating data for online risks. Based on private conversations annotated by participants, sexual risk detection classifiers were created. The results indicated Convolutional Neural Network (CNN) and Random Forest models outperformed in identifying sexual risks at the conversation-level. Our experiments showed that classifiers trained on entire conversations performed better than message-level classifiers. We also trained classifiers to detect the severity risk level of a given message with CNN outperforming other models. We found that contextual (e.g., age, gender, and relationship type) and psycho-linguistic features contributed the most to accurately detecting sexual conversations. Our analysis provides insights into the important factors that enhance automated detection of sexual risks within youths' private conversations.

<p>The thesis consists of three research projects concerning mathematical models for analog computers, originally developed by John Tucker and Jeff Zucker. The models are capable of representing systems that essentially “diverge,” exhibiting no valid behaviour---much the way that digital computers are capable of running programs that never halt. While there is no solution to the general Halting Problem, there are certainly theorems that identify large collections of instances that are guaranteed to halt. For example, if we use a simplified language featuring only assignment, branching, algebraic operations, and loops whose bounds must be fixed in advance (i.e. at “compile time”), we know that all instances expressible in this language will halt.</p> <p>In this spirit, one of the major objectives of all three thesis projects is identify a large class of instances of analog computation (analog computer + input) that are guaranteed to “converge.” In our semantic models, this convergence is assured if a certain operator (representing the computer and its input) has a unique fixed point. The first project is based on an original fixed point construction, while the second and third projects are based on Tucker and Zucker's construction. The second project narrows the scope of the model to a special case in order to concretely identify a class of operators with well-behaved fixed points, and considers some applications. The third project goes the opposite way: widening the scope of the model in order to generalize it.</p> / Doctor of Philosophy (PhD)