abstract: Machine learning methodologies are widely used in almost all aspects of software engineering. An effective machine learning model requires large amounts of data to achieve high accuracy. The data used for classification is mostly labeled, which is difficult to obtain. The dataset requires both high costs and effort to accurately label the data into different classes. With abundance of data, it becomes necessary that all the data should be labeled for its proper utilization and this work focuses on reducing the labeling effort for large dataset. The thesis presents a comparison of different classifiers performance to test if small set of labeled data can be utilized to build accurate models for high prediction rate. The use of small dataset for classification is then extended to active machine learning methodology where, first a one class classifier will predict the outliers in the data and then the outlier samples are added to a training set for support vector machine classifier for labeling the unlabeled data. The labeling of dataset can be scaled up to avoid manual labeling and building more robust machine learning methodologies. / Dissertation/Thesis / Masters Thesis Engineering 2017

Submitted by MARCIO ROBERTO GONÇALVES DE VAZZI null (profmarcio@vazzi.com.br) on 2017-08-23T17:33:30Z No. of bitstreams: 1 Dissertação Marcio 2017_08_23.pdf: 2938261 bytes, checksum: 8035c3e762251cbcf988f6962199a956 (MD5) / Approved for entry into archive by Monique Sasaki (sayumi_sasaki@hotmail.com) on 2017-08-23T19:37:09Z (GMT) No. of bitstreams: 1 vazzi_mrg_me_arafcl.pdf: 2938261 bytes, checksum: 8035c3e762251cbcf988f6962199a956 (MD5) / Made available in DSpace on 2017-08-23T19:37:09Z (GMT). No. of bitstreams: 1 vazzi_mrg_me_arafcl.pdf: 2938261 bytes, checksum: 8035c3e762251cbcf988f6962199a956 (MD5) Previous issue date: 2017-07-17 / Este trabalho investiga a utilização das metodologias ativas como instrumento para o ensino de Física a partir do uso de um “Kit Robótico” desenvolvido com uma plataforma de hardware livre chamada Arduíno. O planejamento do trabalho teve início em 2015, momento em que foi realizada uma revisão da literatura sobre a temática e sobre as metodologias que poderiam a ser adotadas no seu desenvolvimento. Como resultado da revisão teórico-metodológica optamos por implementar o projeto por meio de um conjunto de atividades baseadas na metodologia de Aprendizagem Baseada em Problemas focalizando a temática “Movimento”, dentro do currículo de Física do 1° ano do Ensino Médio de uma escola Técnica do Centro Estadual de Educação Tecnológica Paula Souza. O estudo de campo foi desenvolvido a partir de encontros periódicos com um grupo de alunos que manifestaram interesse pela proposta. Para direcionar as atividades e garantir que os alunos desenvolvessem as competências e habilidades desejadas foi utilizado os denominados os Roteiros de Atividades, que criava situações problemas para contextualizar a aprendizagem. Após a realização da pesquisa, verificamos alguns indícios de que o recursos e metodologias adotados podem auxiliar o professor e contribuir para o processo de ensino e aprendizagem, entretanto, não é a inserção da tecnologia, por si, o elemento fundamental que altera as condições de aprendizagem e sim de saber aproveitar as características que esses recursos possuem para fazer o aluno acreditar na legitimidade dos conteúdos escolares e envolver-se na construção do seu conhecimento. Deste modo, podemos dizer que, a ferramenta tecnológica utilizada nesta pesquisa (Kit Robótico), permitiu desenvolver satisfatoriamente atividades relativas ao tema “Movimento”, que atuou como elemento contextualizador e motivador no processo de aprendizagem. / This work investigates the use of active methodologies as an instrument for teaching physics from the use of a "Robotic Kit" developed with a free hardware platform called Arduino. Work planning began in 2015, at which time a review of the literature on the subject and on the methodologies that could be adopted in its development was carried out. As a result of the theoretical-methodological revision we decided to implement the project through a set of activities based on the methodology of Problem-Based Learning focusing on the theme "Movement" within the Physics curriculum of the 1st year of High School of a Technical School Of the State Center of Technological Education Paula Souza. The field study was developed from periodic meetings with a group of students who expressed an interest in the proposal. In order to direct the activities and ensure that the students developed the desired skills and abilities, we used the so-called Activity Schedules, which created situations to contextualize the learning situations. After conducting the research, we verified some evidence that the resources and methodologies adopted can help the teacher and contribute to the teaching and learning process, however, it is not the insertion of technology, by itself, the fundamental element that changes the conditions of Learning but rather to know how to take advantage of the characteristics that these resources have in order to make the student believe in the legitimacy of the school contents and to be involved in the construction of their knowledge. In this way, we can say that, the technological tool used in this research (Robotic Kit), allowed to develop satisfactorily activities related to the theme "Movement", which acted as contextualizing and motivating element in the learning process.

abstract: The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation. / Dissertation/Thesis / Ph.D. Computer Science 2013

L'apprentissage actif apparaît comme un problème important dans différents contextes de l'apprentissage supervisé pour lesquels obtenir des données est une tâche aisée mais les étiqueter est coûteux. En règle générale, c’est une stratégie de requête, une heuristique gloutonne basée sur un critère de sélection qui recherche les données non étiquetées potentiellement les plus intéressantes pour former ainsi un ensemble d'apprentissage. Une stratégie de requête est donc une procédure d'échantillonnage biaisée puisqu'elle favorise systématiquement certaines observations s'écartant ainsi des modèles d'échantillonnages indépendants et identiquement distribués. L'hypothèse principale de cette thèse s'inscrit dans la réduction du biais introduit par le critère de sélection. La proposition générale consiste à réduire le biais en sélectionnant le sous-ensemble minimal d'apprentissage pour lequel l'estimation de la loi de probabilité est aussi proche que possible de la loi sous-jacente prenant en compte l’intégralité des observations. Pour ce faire, une nouvelle stratégie générale de requête pour l'apprentissage actif a été mise au point utilisant la théorie de l'Information. Les performances de la stratégie de requête proposée ont été évaluées sur des données réelles et simulées. Les résultats obtenus confirment l'hypothèse sur le biais et montrent que l'approche envisagée améliore l'état de l'art sur différents jeux de données. / Active Learning arises as an important issue in several supervised learning scenarios where obtaining data is cheap, but labeling is costly. In general, this consists in a query strategy, a greedy heuristic based on some selection criterion, which searches for the potentially most informative observations to be labeled in order to form a training set. A query strategy is therefore a biased sampling procedure since it systematically favors some observations by generating biased training sets, instead of making independent and identically distributed draws. The main hypothesis of this thesis lies in the reduction of the bias inherited from the selection criterion. The general proposal consists in reducing the bias by selecting the minimal training set from which the estimated probability distribution is as close as possible to the underlying distribution of overall observations. For that, a novel general active learning query strategy has been developed using an Information-Theoretic framework. Several experiments have been performed in order to evaluate the performance of the proposed strategy. The obtained results confirm the hypothesis about the bias, showing that the proposal outperforms the baselines in different datasets.

Increasingly, multimedia technology is permeating the educational arena worldwide, and many colleges and universities are moving towards the use of digital technology to enhance the teaching and learning process of both the students and educational practitioners (Kachian & Wieser, 1999:[online]; Mat, 2000:[online]). South Africa is a developing country that is undergoing radical social, political, economic and cultural changes and advances in computer technology have also dramatically changed the learning and teaching process and provided new learning opportunities and access to educational resources beyond those traditionally available. This research study describes a design experiment in which a multimedia learning environment (MMLE) was crafted for nursing students in neonatology at a university in Johannesburg, South Africa. At the outset, the integrated design principles derived from the constructivist perspectives on learning, multimedia learning design principles generated from Instructional Design Theory and the learning styles according to Kolb’s Learning Style Inventory, are established as a theoretical point of departure. This theoretical position led to the generation of a design framework that exploits the congruencies between constructivist perspectives on learning, the individual attributes of learners as defined by learning styles and multimedia design principles. The design experiment is conducted in five phases: the establishment of the design framework after an extensive literature review, the development of the MMLE, a pilot study, the final implementation and data analysis. Both quantitative and qualitative data are collected. The preliminary results of the study show that the students had an overwhelmingly positive experience of the MMLE, and that their preferred learning style had some influence on their experience. Little evidence has been found in the literature about the interaction between learning styles and constructivist learning principles for the design of multimedia learning and it is in this area that the study makes a contribution. The study also makes a contribution to the field of Nursing Science education, as it designs and develops multimedia learning materials, and assesses the value of those learning materials for learners which may be adopted in similar contexts within the broader South African context. / Prof. D. Van Der Westhuizen

Hierarchical task decompositions play an essential role in the design of complex simulation and decision systems, such as the ones that arise in video games. Game designers find it very natural to adopt a divide-and-conquer philosophy of specifying hierarchical policies, where decision modules can be constructed somewhat independently. The process of choosing the parameters of these modules manually is typically lengthy and tedious. The hierarchical reinforcement learning (HRL) field has produced elegant ways of decomposing policies and value functions using semi-Markov decision processes. However, there is still a lack of demonstrations in larger nonlinear systems with discrete and continuous variables. To narrow this gap between industrial practices and academic ideas, we address the problem of designing efficient algorithms to facilitate the deployment of HRL ideas in more realistic settings. In particular, we propose Bayesian active learning methods to learn the relevant aspects of either policies or value functions by focusing on the most relevant parts of the parameter and state spaces respectively. To demonstrate the scalability of our solution, we have applied it to The Open Racing Car Simulator (TORCS), a 3D game engine that implements complex vehicle dynamics. The environment is a large topological map roughly based on downtown Vancouver, British Columbia. Higher level abstract tasks are also learned in this process using a model-based extension of the MAXQ algorithm. Our solution demonstrates how HRL can be scaled to large applications with complex, discrete and continuous non-linear dynamics. / Science, Faculty of / Computer Science, Department of / Graduate

Data Stream mining is an important emerging topic in the data mining and machine learning domain. In a Data Stream setting, the data arrive continuously and often at a fast pace. Examples include credit cards transaction records, surveillances video streams, network event logs, and telecommunication records. Such types of data bring new challenges to the data mining research community. Specifically, a number of researchers have developed techniques in order to build accurate classification models against such Data Streams. Ensemble Learning, where a number of so-called base classifiers are combined in order to build a model, has shown some promise. However, a number of challenges remain. Often, the class labels of the arriving data are incorrect or missing. Furthermore, Data Stream algorithms may benefit from an online learning paradigm, where a small amount of newly arriving data is used to learn incrementally. To this end, the use of Active Learning, where the user is in the loop, has been proposed as a way to extend Ensemble Learning. Here, the hypothesis is that Active Learning would increase the performance, in terms of accuracy, ensemble size, and the time it takes to build the model. This thesis tests the validity of this hypothesis. Namely, we explore whether augmenting Ensemble Learning with an Active Learning component benefits the Data Stream Learning process. Our analysis indicates that this hypothesis does not necessarily hold for the datasets under consideration. That is, the accuracies of Active Ensemble Learning are not statistically significantly higher than when using normal Ensemble Learning. Rather, Active Learning may even cause an increase in error rate. Further, Active Ensemble Learning actually results in an increase in the time taken to build the model. However, our results indicate that Active Ensemble Learning builds accurate models against much smaller ensemble sizes, when compared to the traditional Ensemble Learning algorithms. Further, the models we build are constructed against small and incrementally growing training sets, which may be very beneficial in a real time Data Stream setting.

Active learning is a common solution for reducing labeling costs and maximizing the impact of human labeling efforts in binary and multi-class classification settings. However, when we are faced with extreme levels of class imbalance, a situation in which it is not safe to assume that we have a representative sample of the minority class, it has been shown effective to replace the binary classifiers with a one-class classifiers. In such a setting, traditional active learning methods, and many previously proposed in the literature for one-class classifiers, prove to be inappropriate, as they rely on assumptions about the data that no longer stand. In this thesis, we propose a novel approach to active learning designed for one-class classification. The proposed method does not rely on many of the inappropriate assumptions of its predecessors and leads to more robust classification performance. The gist of this method consists of labeling, in priority, the instances considered to fit the learned class the least by previous iterations of a one-class classification model. Throughout the thesis, we provide evidence for the merits of our method, then deepen our understanding of these merits by exploring the properties of the method that allow it to outperform the alternatives.

This thesis is a record of two research strands that have been intertwined during the development over a four year period of a classroom curriculum for an elective Geology 12 course in a new school. It discusses traditional belief systems identified as common to the practice of senior science and how one teacher wanted to challenge those beliefs to produce a working curriculum that would focus on long term learning within the framework of an externally prescribed curriculum and a provincially mandated external final exam that counted for 40% of the students mark. The teacher, working on her own in a portable for the first two years was in the unenviable position of being supplied with textbooks with a foreign focus and with supplies that as the title suggests were of little use over the long term. By Christmas of the first year a number of major problems had been identified, these problems falling into two major categories - developing strategies for long term learning that, within the operational constraints of grade 12, would enable the students to take far more responsibility for their own learning, and second, developing a science research programme for acquiring the resources, principally through field work, that were identified as being necessary for the programme. The major concerns within these two problem areas were identified and a four year timeline was developed for implementation. On the pedagogical side, after examining some of the literature on learning, particularly that around the area of cooperative learning that has had a substantial focus in recent years in a number of local school districts, reflecting on what worked for me in terms of my practice over 27 years of science teaching, I chose to focus on the Project for Enhancing Effective Learning (PEEL), out of Monash University, Australia as my working framework for learning. The process of developing this classroom curriculum was framed as a qualitative individual action research project over time as, within my professional life, there were no other teachers involved with the geology programme within the school, and at the same time being in a portable isolated me from my peers-l had no choice but to be self contained and self reliant. The pedagogical side of the process saw the evolvement of a programme that differed significantly in many ways from traditional senior science teaching. This is not to say that many teachers are not already reflecting on and trying to improve practice but for most of them it is through quiet reflection, discourse and evolution much as it had been for me until this time. For me this was the first time in my career that I was able to develop a programme from the very beginning. The thesis details the development of a multi-level learning strategy with an underlying theme being the development of more metacognitive students. The programme entails the identification of prior learning, reflective and collaborative practice, multiple processings of content and skills, peer assessment, and semi-formal reflective assessment. For many students, particularly during my first two years, most of these strategies were completely foreign to their cultural expectations of the teacher's role as dispenser of information to be regurgitated back through formal assessment. During the last two years these challenges to student thinking have been far less dramatic as I am now a known quantity in the school and the students taking my course expect to be working at becoming more independent long term learners. The programme is also built on the premise that for geology, relevant hands-on activities are an integral part of the learning process, and this other research strand is also explored and described. This is the story of the two research strands by which a semi-independent multi-level learning environment has been developed and implemented with a high degree of hands-on activities. Although a formal assessment of the programme is almost impossible to do within the constraints of my working environment, the personal feedback that I receive from the students, parents and colleagues indicates that it has been successful. / Education, Faculty of / Curriculum and Pedagogy (EDCP), Department of / Graduate

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