Doctor of Philosophy / Department of Electrical and Computer Engineering / Sanjoy Das and Anil Pahwa / Because a majority of day-to-day activities rely on electricity, it plays an important role in daily life. In this digital world, most of the people’s life depends on electricity. Without electricity, the flip of a switch would no longer produce instant light, television or refrigerators would be nonexistent, and hundreds of conveniences often taken for granted would be impossible. Electricity has become a basic necessity, and so any interruption in service due to disturbances in power lines causes a great inconvenience to customers. Customers and utility commissions expect a high level of reliability. Power distribution systems are geographically dispersed and exposure to environment makes them highly vulnerable part of power systems with respect to failures and interruption of service to customers. Following the restructuring and increased competition in the electric utility industry, distribution system reliability has acquired larger significance. Better understanding of causes and consequences of distribution interruptions is helpful in maintaining distribution systems, designing reliable systems, installing protection devices, and environmental issues. Various events, such as equipment failure, animal activity, tree fall, wind, and lightning, can negatively affect power distribution systems. Weather is one of the primary causes affecting distribution system reliability. Unfortunately, as weather-related outages are highly random, predicting their occurrence is an arduous task. To study the impact of weather on overhead distribution system several models, such as linear and exponential regression models, neural network model, and ensemble methods are presented in this dissertation. The models were extended to study the impact of animal activity on outages in overhead distribution system. Outage, lightning, and weather data for four different cities in Kansas of various sizes from 2005 to 2011 were provided by Westar Energy, Topeka, and state climate office at Kansas State University weather services. Models developed are applied to estimate daily outages. Performance tests shows that regression and neural network models are able to estimate outages well but failed to estimate well in lower and upper range of observed values. The introduction of committee machines inspired by the ‘divide & conquer” principle overcomes this problem. Simulation results shows that mixture of experts model is more effective followed by AdaBoost model in estimating daily outages. Similar results on performance of these models were found for animal-caused outages.

Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable.

Through the development and implementation of modified learning contexts, the current study encouraged undergraduate teacher education students to modify their approaches to learning by reducing their reliance on surface approaches and progressively adopting deeper approaches. This outcome was considered desirable because students who employed deep approaches would exit the course having achieved higher quality learning than those who relied primarily on surface approaches. It was expected that higher quality learning in a preservice teacher education program would also translate into greater self-confidence in the management of teaching tasks, leading to improvements in students� teaching self-efficacy beliefs. Altered learning contexts were developed through the application of action research methodology involving core members of the teaching team. Learning activities were designed with a focus on co-operative small-group problem-based learning, which included multiple subtasks requiring variable outcome presentation modes. Linked individual reflection was encouraged by personal learning journals and learning portfolios. Students also provided critical analyses of their own learning during the completion of tasks, from both individual and group perspectives. Assessment methods included lecturer, peer and self-assessment, depending on the nature of the learning task. Often these were integrated, so that subtasks within larger ones were assessed using combinations of methods. Learning approach theorists (Biggs, 1993a, 1999; Entwistle, 1986, 1998; Prosser & Trigwell, 1999; Ramsden, 1992, 1997) contend that learning outcomes are directly related to the learning approaches used in their development. They further contend that the approach adopted is largely a result of students� intent, which in turn, is influenced by their perception of the learning context. The present study therefore aimed to develop an integrated and pervasive course-based learning context, constructively aligned (after: Biggs, 1993a, 1996), achievable within the normal constraints of a university program, that would influence students� adoption of deep learning approaches. The cognitive processes students used in response to the altered contexts were interpreted in accordance with self-regulatory internal logic (after: Bandura, 1986, 1991b; Zimmerman, 1989, 1998b). Longitudinal quasi-experimental methods with repeated measures on non-equivalent dependent variables were applied to three cohorts of students. Cohort 1 represented the contrast group who followed a traditional program. Cohort 2 was the main treatment group to whom the modified program was presented. Cohort 3 represented a comparison group that was also presented with the modified program over a shorter period. Student data on learning approach, teaching efficacy and academic attributions were gathered from repeated administrations of the Study Process Questionnaire (Biggs, 1987b), Teacher Efficacy Scale (Gibson & Dembo, 1984) and Multidimensional-Multiattributional Causality Scale (Lefcourt, 1991). In addition, reflective journals, field observations and transcripts of interviews undertaken at the beginning and conclusion of the course, were used to clarify students� approaches to learning and their responses to program modifications. Analyses of learning approaches adopted by Cohorts 1 and 2 revealed that they both began their course predominantly using surface approaches. While students in Cohort 1 completed the course with approximately equal reliance on deep and surface approaches, students in Cohort 2 reported a predominant use of deep approaches on course completion. The relative impact of the modified learning context on students with differing approaches to learning in this cohort were further explained through qualitative data and cluster analyses. The partial replication of the study with Cohort 3, across the first three semesters of their program, produced similar effects to those obtained with Cohort 2. The analyses conducted with teaching efficacy data indicated a similar pattern of development for all cohorts. Little change in either personal or general dimensions was noted in the first half of the program, followed by strong growth in both, in the latter half. While a relationship between learning approach usage and teaching efficacy was not apparent in Cohort 1, developmental path and mediation analyses indicated that the use of deep learning approaches considerably influenced the development of personal teaching efficacy in Cohort 2. The current research suggests that value lies in the construction of learning environments, in teacher education, that enhance students� adoption of deep learning approaches. The nature of the task is complex, multifaceted and context specific, most likely requiring the development of unique solutions in each environment. Nevertheless, this research demonstrates that such solutions can be developed and applied within the prevailing constraints of pre-existing course structures.

<p><strong>Purpose:</strong> my aim was to explore ways that teachers can support students with dyslexia and what/ which tools some teachers / special education teachers use to facilitate students.</p><p><strong>Method:</strong> I used a quantitative method by interviewing some regular teachers and special education teachers</p><p><strong>Results:</strong> In my study, I learned how some teachers can support students by reading loud to them; a main task for the teachers could be to create the love of reading for the students. That task was reinforced by students' self-image.</p>

The goal of this thesis is to develop better practical methods for detecting common object classes in real world images. We present a family of object detectors that combine Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) features with efficient Latent SVM classifiers and effective dimensionality reduction and sparsification schemes to give state-of-the-art performance on several important datasets including PASCAL VOC2006 and VOC2007, INRIA Person and ETHZ. The three main contributions are as follows. Firstly, we pioneer the use of Local Ternary Pattern features for object detection, showing that LTP gives better overall performance than HOG and LBP, because it captures both rich local texture and object shape information while being resistant to variations in lighting conditions. It thus works well both for classes that are recognized mainly by their structure and ones that are recognized mainly by their textures. We also show that HOG, LBP and LTP complement one another, so that an extended feature set that incorporates all three of them gives further improvements in performance. Secondly, in order to tackle the speed and memory usage problems associated with high-dimensional modern feature sets, we propose two effective dimensionality reduction techniques. The first, feature projection using Partial Least Squares, allows detectors to be trained more rapidly with negligible loss of accuracy and no loss of run time speed for linear detectors. The second, feature selection using SVM weight truncation, allows active feature sets to be reduced in size by almost an order of magnitude with little or no loss, and often a small gain, in detector accuracy. Despite its simplicity, this feature selection scheme outperforms all of the other sparsity enforcing methods that we have tested. Lastly, we describe work in progress on Local Quantized Patterns (LQP), a generalized form of local pattern features that uses lookup table based vector quantization to provide local pattern style pixel neighbourhood codings that have the speed of LBP/LTP and some of the flexibility and power of traditional visual word representations. Our experiments show that LQP outperforms all of the other feature sets tested including HOG, LBP and LTP.

“Tell me and I forget, show me and I remember, involve me and I understand.” - Chinese Proverb. Involvement in learning implies possessing skills and attitudes that permit students to seek resolutions to questions and issues while constructing new knowledge. Low enrollment in Plant Biology and Diversity and upper level plant science courses has been noticed at Western Kentucky University. In addition, graduating students performed below the national average on the senior assessment examination in the area of botany content knowledge offered by WKU’s Biology Department. This may be due to the fact that observation-based botany has been taught in a traditional way for biology majors at our university for many years. Traditional teaching methods include viewing prepared slides of plant sections, viewing live and herbarium specimens, and memorization of botanical terminology and illustrations. The goal of this study is to convert these existing traditional laboratories to investigative inquiry exercises without compromising the material covered by bringing observation-based labs into the twenty-first century. Various teaching strategies including inquiry, problem-based, case-based, and hands-on learning methods were implemented. Each exercise was reshaped around a central question or theme. These changes were expected to increase student learning and retention levels. Traditional teaching methods were used with the control group, while contemporary teaching strategies were used with the experimental set of students. Traditional assessments and anonymous surveys were statistically analyzed. The results of my analyses suggest that the experimental students were more challenged, interested, intellectually stimulated and less overwhelmed with contemporary teaching strategies and overall had higher learning retention demonstrated by their performance on assessments. Moreover, I predicted that an investigative approach will encourage larger numbers of students to take this restricted elective sophomore-level course for biology majors and further their study in plant biology.

Purpose: my aim was to explore ways that teachers can support students with dyslexia and what/ which tools some teachers / special education teachers use to facilitate students. Method: I used a quantitative method by interviewing some regular teachers and special education teachers Results: In my study, I learned how some teachers can support students by reading loud to them; a main task for the teachers could be to create the love of reading for the students. That task was reinforced by students' self-image.

Στη διπλωματική αυτή παρουσιάζεται μια μεθοδολογία για την ταξινόμηση των μουσικών κομματιών και τραγουδιών ανάλογα με το μουσικό είδος που ανήκουν. Συγκεκριμένα χρησιμοποιούνται τα ίδια τα μουσικά κομμάτια ως πηγή πληροφοριών, και μέσω γνωστών μεθόδων επεξεργασίας ακουστικών σημάτων (που βασίζονται στο μετασχηματισμό Fourier) εξάγονται κατάλληλα δεδομένα. Στη συνέχεια με χρήση γνωστών μεθόδων μάθησης με επίβλεψη (δέντρα αποφάσεων, σύνολα κανόνων, μπεϊσιανή μάθηση, τεχνητά νευρωνικά δίκτυα και μηχανές διανυσμάτων υποστήριξης), γίνεται η αντίστοιχη μουσική ταξινόμηση. Στο τέλος προτείνεται μια συνδυαστική μέθοδος ταξινόμησης (συνδυασμός αλγορίθμων μάθησης) για την βελτίωση των αποτελεσμάτων όπου θα βγουν και τα τελικά συμπεράσματα. / The present work describes a methodology for the automatic recognition of music genres, based exclusively on the audio content of the signal. We use proposed techniques to extract attributes (based on Fourier Transform). Then with familiar supervised classification techniques we classify seven different music genres (rock, metal, hard rock, classical music, jazz, beat, rempetika -rempetika is a Greek music genre). At the end we propose a combined technique to improve our results. This technique is based on stacking generalization. We propose an improvement of stacking generalization.

Darbe analizuojami įvairūs metodai, tinkami mokyti(s) matematikos nuotoliniu būdu. Išnagrinėjus ŠMM dokumentus ir pedagoginę literatūrą, daroma išvada, jog nuotoliniam matematikos dalyko mokymui yra tinkamas mišrusis dalyko mokymas,kuomet tarpusavyje derinami programuotas mokymas (įgūdžių įtvirtinimui, turinio diferencijavimui) bei tradiciniai metodai (naujos medžiagos aiškinimui, konsultavimui). / In teaching mathematics it is not enough to realize the only one method. The analysis of scientific literature has opened, that for distance learning of mathematics it would be effective to realize composite method there programmed learning would be combined with traditional methods.

Remiantis nauju supratimu, mokymas(is) laikomas aktyviu procesu, kurio metu besimokantysis, remdamasis anksčiau įgytomis žiniomis ir savo unikalia patirtimi, formuoja naujas sąvokas, idėjas ar prasmes. Mokytojo vaidmuo suprantamas kaip pagalbininko, kuris turi rūpintis besimokančiojo žinių kūrimo procesu,o taip pat bendraudamas ir stebėdamas besimokančiuosius, lanksčiai ir kūrybingai įtraukti juos į mokymo(si) procesą. Šio darbo tikslas: pagrįsti aktyvaus mokymo įtaką mokinių mokymosi motyvacijai. / Nowadays theories say, that learning is an active method, where the teacher is a part of this process, like an assitent communicating with the pupils. Also nowadays theories say, that it is not enough give new information, it is important include practise. Some methods were used to evalue correlations these factors.