Intrusion detection systems are an integral part of web application security. As Internet use continues to increase, the demand for fast, accurate intrusion detection systems has grown. Various IDSs like Snort, Zeek, Solarwinds SEM, and Sleuth9, detect malicious intent based on existing patterns of attack. While these systems are widely deployed, there are limitations with their approach, and anomaly-based IDSs that classify baseline behavior and trigger on deviations were developed to address their shortcomings. Existing anomaly-based IDSs have limitations that are typical of any machine learning system, including high false-positive rates, a lack of clear infrastructure for deployment, the requirement for data to be centralized, and an inability to add modules tailored to specific organizational threats. To address these shortcomings, our work proposes a system that is distributed in nature, can actively learn and uses experts to improve accuracy. Our results indicate that the integrated system can operate independently as a holistic system while maintaining an accuracy of 99.03%, a false positive rate of 0.5%, and speed of processing 160,000 packets per second for an average system. / Master of Science / Intrusion detection systems are an integral part of web application security. The task of an intrusion detection system is to identify attacks on web applications. As Internet use continues to increase, the demand for fast, accurate intrusion detection systems has grown. Various IDSs like Snort, Zeek, Solarwinds SEM, and Sleuth9, detect malicious intent based on existing attack patterns. While these systems are widely deployed, there are limitations with their approach, and anomaly-based IDSs that learn a system's baseline behavior and trigger on deviations were developed to address their shortcomings. Existing anomaly-based IDSs have limitations that are typical of any machine learning system, including high false-positive rates, a lack of clear infrastructure for deployment, the requirement for data to be centralized, and an inability to add modules tailored to specific organizational threats. To address these shortcomings, our work proposes a system that is distributed in nature, can actively learn and uses experts to improve accuracy. Our results indicate that the integrated system can operate independently as a holistic system while maintaining an accuracy of 99.03%, a false positive rate of 0.5%, and speed of processing 160,000 packets per second for an average system.

Electronic textbooks are different from ebooks (electronic books) in that they allow the user to go beyond just reading material on a computer screen. Electronic textbooks encourage the user to accomplish all of the operations typically performed with a hardcopy text in addition to some functions not possible with paper books. With electronic textbooks users can make annotations in the textbook with e-ink; mark important sections; search over the ink, the text, or even the scanned images; look up items in online dictionaries or encyclopedias; perform interactive reinforcement drills; view simulations; and many other operations afforded by the computing power of the underlying computer and the reach of the Internet connection. These operations encourage students to engage in active reading. The VText framework is designed to provide many of the desired features of an e-textbook in such a way that it produces pedagogical value rather than just convenience for students. Many so-called e-textbook solutions available today provide few features beyond those possible with hardcopy textbooks. The VText framework is built as an add-in to Microsoft's note-taking program, OneNote. The add-in provides features that facilitate the use of OneNote as a reader and as an educational tool while leaving in place OneNote's strengths in note-taking, collaboration, and search. / Master of Science

This study examined the effect of active learning seminars and a mentoring program on the use of active learning teaching techniques by college faculty. A quasi-experimental study was conducted using convenience samples of faculty from two private Christian supported institutions. Data for the study were collected from surveys and faculty course evaluations. The study lasted one semester. Faculty volunteers from one institution served as the experimental group and faculty volunteers from the second institution were the comparison group. The experimental group attended approximately eight hours of active learning seminars and also participated in a one-semester mentoring program designed to assist faculty in application of active learning techniques. Several individuals conducted the active learning seminars. Dr. Charles Bonwell, a noted authority on active learning, conducted the first three-hour seminar. Seven faculty who had successfully used active learning in their classrooms were selected to conduct the remaining seminars. The faculty-mentoring program was supervised by the researcher and conducted by department chairs. Data were collected from three surveys and faculty course evaluations. The three surveys were the Faculty Active Learning Survey created by the researcher, the Teaching Goals Inventory created by Angelo and Cross, and the college edition of Learner-Centered Practices by Barbara McCombs. The use of active learning techniques by the experimental group increased significantly more than the use by those in the convenience sample. No statistical difference was found in the change of professors' teaching beliefs or the course evaluation results.

Using methodologies of interviews and observation, this study focuses on three distinctive and successful kinds of experiential education: a summer arts program, an outdoor science program and a wilderness education program. The project applies insights from the programs to the central question of this thesis: How can experiential learning be utilized within the traditional classroom given the constraints of the No Child Left Behind Law and standardized teaching?

In this study the status of the active learning methods of teaching employed in Teachers‟ Training Colleges in Ethiopia was examined from the perspective of the trainee-teachers and their lecturers. Factors relating to active learning were discussed within the framework of the social-cognitive constructivists‟ theory, as learning (knowledge construction) requires the direct involvement of an individual, as well as interaction with the social environment. Three main groups of learning theories were investigated, namely the behavioural, the cognitive and the constructivist theories. The behavioural theories emphasise the transmission of information from the teacher to the learner, whereas the cognitive and constructivist theories emphasise the learners‟ construction of knowledge from their own experiences and their interaction with one another. The methods and strategies for teaching Geography in Ethiopian Teachers‟ Training Colleges were also discussed. The empirical research investigated the perceptions of trainee-teacher and lecturers regarding active learning methods at their respective colleges, the current teaching-learning methods and strategies that the trainee-teachers employ, how they experience the current teaching methods and approaches and other related matters, such as class size and facilities, the integration of assessment with active learning, the teachers‟ attitudes towards active learning, whether or not the lecturers receive short-and long-term training on active learning, the support obtained from the managing body, and whether or not any guidelines existed. Interviews were conducted as embedded research that was intended to supplement the quantitative findings. The results of the empirical investigation indicated a lack of systematic and appropriately organised active learning at college level. Some of the barriers that profoundly hindered the use of active learning pedagogies are the lecturers‟ tendency to maintain the traditional (lecture) method of teaching, insufficient pre-service and in-service training, the large class sizes, the lack of administrative support, the scarcity of resources/facilities, the lack of integration between assessment and active learning, and finally, the absence of guidelines. Pertinent information was extracted from the relevant literature and the empirical study to develop a model to address the problem of active learning in Teachers‟ Training Colleges. Thus, a Model of Active Learning, which is relevant to the teaching of the learning of Geography at College level in the Ethiopian context, was developed. / Curriculum and Instructional Studies / D. Ed. (Curriculum Studies)

published_or_final_version / Social Work and Administration / Doctoral / Doctor of Philosophy

This thesis contains the research findings from my participation in two research projects. The first is the development and assessment of Active Learning Modules (ALMs) for engineering students. The ALMs assist students in learning complex Finite Element Analysis (FEA) principles. We measure the effectiveness of the modules by issuing pre- and post-module quizzes and analyze the differences of the quiz scores. Active learning modules are used to meet the needs of all students’ learning styles. Each student who uses an ALM takes a series of learning style assessment quizzes (MBTI, LIS …). We statistically compare the learning styles and quiz scores to ensure all learning styles are improving equally well. In cases where they are not, we created a tool to make suggestions to the ALM developer on how to adjust the ALM to meet the needs of the outlying learning style group(s). Following modification, the implementation and evaluation process of the ALM is repeated. My second area of research focused on the development of a concise prototype strategy development tool. This tool guides engineering product development teams through six critical prototype strategy choices: (1) How many concepts should be prototyped? (2) How many iterations of a concept should be built? (3) Should the prototype be virtual or physical? (4) Should subsystems be isolated? (5) Should the prototype be scaled? (6) Should the design requirements be temporarily relaxed? This list of choices is not comprehensive but served as a starting point for this groundbreaking research. The tool was tested at The University of Texas at Austin and the United States Air Force Academy. Results indicate the method did improve students’ performance across a number of assessment metrics. / text

Data generation has vastly exceeded manual annotation in several areas of astronomy, biology, economy, geology, medicine and physics. At the same time, a public community of experts and hobbyists has developed around some of these disciplines thanks to open, editable web resources such as wikis and public annotation challenges. In this thesis I investigate under which conditions a combination of collaborative and automated curation could complete annotation tasks unattainable by human curators alone. My exemplar curation process is taken from the molecular biology domain: the association all existing enzymes (proteins catalysing a chemical reaction) with their function. Assigning enzymatic function to the proteins in a genome is the first essential problem of metabolic reconstruction, important for biology, medicine, industrial production and environmental studies. In the protein database UniProt, only 3% of the records are currently manually curated and only 60% of the 17 million recorded proteins have some functional annotation, including enzymatic annotation. The proteins in UniProt represent only about 380,000 animal species (2,000 of which have completely sequenced genomes) out of the estimated millions of species existing on earth. The enzyme annotation task already applies to millions of entries and this number is bound to increase rapidly as sequencing efforts intensify. To guide my analysis I first develop a basic model of collaborative curation and evaluate it against molecular biology knowledge bases. The analysis highlights a surprising similarity between open and closed annotation environments on metrics usually connected with “democracy” of content. I then develop and evaluate a method to enhance enzyme function annotation using machine learning which demonstrates very high accuracy, recall and precision and the capacity to scale to millions of enzyme instances. This method needs only a protein sequence as input and is thus widely applicable to genomic and metagenomic analysis. The last phase of the work uses active and guided learning to bring together collaborative and automatic curation. In active learning a machine learning algorithm suggests to the human curators which entry should be annotated next. This strategy has the potential to coordinate and reduce the amount of manual curation while improving classification performance and reducing the number of training instances needed. This work demonstrates the benefits of combining classic machine learning and guided learning to improve the quantity and quality of enzymatic knowledge and to bring us closer to the goal of annotating all existing enzymes.

Diagnosis is the first analysis step for uncovering the root cause of failure for a defective chip. It is a fast and non-destructive approach to preliminarily identify and locate possible defects in a failing chip. Despite many advances in diagnosis techniques, it is often the case, however, that resolution, i.e., the number of locations or candidates reported by diagnosis, exceeds the number of actual failing locations. To address this major challenge, a novel, machine-learning-based resolution improvement methodology named PADRE (Physically-Aware Diagnostic Resolution Enhancement) is described. PADRE uses easily-available tester and simulation data to extract features that uniquely characterize each candidate. PADRE applies machine learning to the features to identify candidates that correspond to the actual failure locations. Through various experiments, PADRE is shown to significantly improve resolution with virtually no negative impact on accuracy. Specifically, in simulation experiments, the number of defects that have perfect resolution is increased by 5x with little degradation of accuracy. An important investigation that typically follows diagnosis is Physical Failure Analysis (PFA), which can also provide information that is helpful for improving diagnosis. PADRE influences PFA within a novel, active learning (AL) based PFA selection approach. An active-learning based PADRE (AL PADRE) selects the most useful defects for PFA in order to improve diagnostic resolution. Experiments show AL PADRE can reach an accuracy of 90% with 60% less PFA, on average, compared to conventional defect selection for PFA. In addition, during the yield learning process, the failing mechanisms that lead to defective chips may change due to perturbations in the fabrication process. It is important for PADRE to perform robustly through the entire yield learning process. Therefore, additional techniques are developed to monitor the effectiveness of PADRE in real time, as well as to update PADRE efficiently and stably to cope with changing failure mechanisms.

The concept of flipped learning has received significant attention in recent years. In a flipped learning methodology, students view instructional videos and complete related assignments before class, so that face-to-face time with the instructor can be spent applying the knowledge and skills they were introduced to in the pre-class assignments. This study aims to determine the effectiveness of a flipped learning method for teaching information literacy (IL) skills to undergraduate students compared to a traditional teaching method where the majority of face-to-face time is spent instructing. To evaluate this, a mixed methods research design was used, where results from qualitative interviews helped explain findings from test data, assignment completion data, and major paper rubric data. The IL tests resulted in a small but insignificant test score improvement for flipped participants. Interviewed flipped participants reported mainly positive feelings toward flipped learning, whereas all flipped ESL interviewees related strong positive feedback towards flipped learning. / Graduate / 0515 / 0727 / 0710 / rmccue@uvic.ca