Thesis (M.S.)--Brigham Young University. Dept of Computer Science, 2006. / Includes bibliographical references (p. 69-70).

Thesis (Ph.D) - University of Queensland, 2003. / Includes bibliography.

Thesis (Ed. D.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on September 28, 2007) Vita. Includes bibliographical references.

Automated human activity recognition has attracted increasing attention in the past decade. However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. We introduce the semi-supervised virtual evidence boosting (sVEB)algorithm for training CRFs — a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. sVEB takes advantage of the unlabeled data via mini-mum entropy regularization. The objective function combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. The sVEB algorithm reduces the overall system cost as well as the human labeling cost required during training, which are both important considerations in building real world inference systems. Moreover, we propose an active learning algorithm for training CRFs is based on virtual evidence boosting and uses entropy measures. Active virtual evidence boosting (aVEB) queries the user for most informative examples, efficiently builds up labeled training examples and incorporates unlabeled data as in sVEB. aVEB not only reduces computational complexity of training CRFs as in sVEB, but also outputs more accurate classification results for the same fraction of labeled data. Ina set of experiments we illustrate that our algorithms, sVEB and aVEB, benefit from both the use of unlabeled data and automatic feature selection, and outperform other semi-supervised and active training approaches. The proposed methods could also be extended and employed for other classification problems in relational data. / Science, Faculty of / Computer Science, Department of / Graduate

This phenomenological study explores how certain “innovative” pedagogies were experienced by a group of Chinese students studying Business Management at a mid-ranking UK university. Analysis of the transcripts of interviews (some in Chinese) with 24 students using NVivo shows that whilst most students felt that Active Learning pedagogies effectively supported their learning, for some students the “zone of indeterminacy” in which group projects and simulations were carried out was an uncomfortable space. Salient aspects of these students’ experiences were language, relationships and metacognitive skills, and the discussion explores the way in which these three experiential themes can be conceptualised as interrelated elements of the action (Biesta, 2006) which takes place in Active Learning classrooms. The following recommendations are made: HEIs should attempt to provide students with the advanced skills of negotiation which they will need to use in the flexible, ill-structured environments associated with Active Learning pedagogies; tutors should develop consistent approaches to collaborative assignments focussing on group work processes as well as task completion; the development of metacognitive skills through Active Learning pedagogies should be promoted through the use of explicit reflective elements embedded within the teaching, learning and assessment activities. The concluding discussion proposes that the successful use of Active Learning pedagogies requires a reconceptualisation of the purpose of education and that these pedagogies provide a potential readjustment of the balance between the functions of qualification, socialisation and subjectification (Biesta, 2010).

No description available.

Anomaly detection is the process of finding unusual events in a given dataset. Anomaly detection is often performed on datasets with a fixed set of predefined features. As a result of this, if the normal features bear a close resemblance to the anomalous features, most anomaly detection algorithms exhibit poor performance. This work seeks to answer the question, can we deform these features so as to make the anomalies standout and hence improve the anomaly detection outcome? We employ a Deep Learning and an Active Learning framework to learn features for anomaly detection. In Active Learning, an Oracle (usually a domain expert) labels a small amount of data over a series of training rounds. The deep neural network is trained after each round to incorporate the feedback from the Oracle into the model. Results on the MNIST, CIFAR-10 and Galaxy Zoo datasets show that our algorithm, Ahunt, significantly outperforms other anomaly detection algorithms used on a fixed, static, set of features. Ahunt can therefore overcome a poor choice of features that happen to be suboptimal for detecting anomalies in the data, learning more appropriate features. We also explore the role of the loss function and Active Learning query strategy, showing these are important, especially when there is a significant variation in the anomalies.

Active participation in the learning process enhance students’ critical thinking and problem-solving skills. We implemented peerocused, active learning, recitation sessions with the large-enrollment sections for General Chemistry I courses at Mississippi State University (MSState) over a period of four semesters beginning in Spring 2016. The peerocused recitation program was a success improving student final (standardized ACS) exam scores, pass/fail rates for the course, and continuation on to General Chemistry II (CH 1223) courses. Peerocused collaborative learning and students possessing ownership over their learning significantly enhanced academic outcomes of our program. Worked-example effect is the best known and apparently the most effective cognitive load reducing technique. We incorporated a modified version of worked examples, employing “incorrect worked examples” and studied the impact of incorrect worked examples vs correct worked examples. We hypothesized that looking for errors in incorrect worked examples would achieve greater attention and would prompt students to actively engage on calculation steps than correct worked examples. Eye-tracking results showed that incorrect worked example format was effective at obtaining student attention and engaging students actively on calculation steps. Survey results showed that incorrect worked example format inspired students’ motivation and enhanced student engagement and attentiveness to examine the worked examples intensively. This research provided insights on student focus while reading and learning chemistry worked examples, and opened new avenues for supporting online learning and usage of tablet PC in the learning process. Laboratory experiments provide students the opportunity to obtain hands-on experience on laboratory techniques and instrumentation. We created a biochemistry laboratory course (CH4990) for third-year chemistry major undergraduate students at Mississippi State University. I wrote the biochemistry lab manual consisting of eleven experiments, which involved protein and DNA extraction, ion-exchange chromatography, UV/vis spectroscopy, SDS PAGE electrophoresis, and enzyme kinetics experiments. A new laboratory experiment was incorporated which allowed students exposure to peptide sequencing and proteomics experiments in conjunction with mass spectrometry. The CH4990 biochemistry lab course is open for enrollment in Fall semesters since Fall 2018.

No description available.

Location-based services have experienced substantial growth in the last decade. However, despite extensive research efforts, sub-meter location accuracy with low-cost infrastructure continues to be elusive. Comparing with infrastructure-based solutions, infrastructure-free indoor localization has the major advantage of avoiding extra cost for infrastructure deployment. There are two typical types of infrastructure-free indoor localization solutions, i.e., Pedestrian Dead Reckoning (PDR)-based and fingerprint-based. PDR-based solutions rely on inertial measurement units to estimate the user's relative location. Despite the effort, many issues still remain in PDR systems. For example, any deployed smartphone-based PDR system needs to cope with the changing orientation of smartphone that the phone might be putting in a pocket, or being taken out to use, etc. In addition, the outputs of Micro Electro-Mechanical Systems (MEMS) sensors on smart devices vary over time which results in rapidly accumulated localization errors without external references. Fingerprint-based solutions utilize different types of location dependent parameters to estimate user's absolute location. Although fingerprint-based solutions are usually more practical than PDR-based, they suffer from laborious site survey process. In this dissertation, we aim to mitigate these challenges. First of all, illumination intensity is introduced as a new type of fingerprints to provide location references for PDR-based indoor localization. We propose IDyLL -- an indoor localization system using inertial measurement units (IMU) and photodiode sensors on smartphones. Using a novel illumination peak detection algorithm, IDyLL augments IMU-based pedestrian dead reckoning with location fixes. Moreover, we devise a burned-out detection algorithm for simultaneous luminary-assisted IPS and burned-out luminary detection. Experimental study using data collected from smartphones shows that IDyLL is able to achieve high localization accuracy at low costs. As for fingerprint collection, several frameworks are proposed to ease the laborious site survey process, without compromising fingerprint quality. We propose TuRF, a path-based fingerprint collection mechanism for site survey. MobiBee, a treasure hunt game, is further designed to take advantage of gamification and incentive models for fast fingerprint collection. Motivated by applying mobile crowdsensing for fingerprint collection, we propose ALSense, a distributed active learning framework, for budgeted mobile crowdsensing applications. Novel stream-based active learning strategies are developed to orchestrate queries of annotation data and the upload of unlabeled data from mobile devices. Extensive experiments demonstrate that ALSense can indeed achieve higher classification accuracy given fixed data acquisition budgets. Facing malicious behaviors, three types of location-related attacks and their corresponding detection algorithms are investigated. Experiments on both crowdsensed and emulated dataset show that the proposed algorithms can detect all three types of attacks with high accuracy. / Thesis / Doctor of Philosophy (PhD)