Adding traceability to an educational IDE : a thesis presented in partial fulfilment of the requirements for the Master degree in Computer Science at Massey University, Manawatu, New Zealand

High dropout and failure rate in introductory programming courses indicate the need to improve programming comprehension of novice learners. Some of educational tools have successfully used game environments to motivate students. Our approach is based on a novel type of notional machine which can facilitate programming comprehension in the context of turn-based games. The first aim of this project is to design a layered notional machine that is reversible. This type of notional machine provides bi-directional traceability and supports multiple layers of abstraction. The second aim of this project is to explore the feasibility and in particular to evaluate the performance of using the traceability in a web-based environment. To achieve these aims, we implement this type of notional machine through instrumentation and investigate the capture of the entire execution state of a program. However, capturing the entire execution state produces a large amount of tracing data that raises scalability issues. Therefore, several encoding and compression methods are proposed to minimise the server work-load. A proof-of-concept implementation which based on the SoGaCo educational web IDE is presented. The evaluation of the educational benefits and end user studies are outside the scope of this thesis

FAME: Face Association through Model Evolution

We attack the problem of learning face models for public faces from weakly-labelled images collected from web through querying a name. The data is very noisy even after face detection, with several irrelevant faces corresponding to other people. We propose a novel method, Face Association through Model Evolution (FAME), that is able to prune the data in an iterative way, for the face models associated to a name to evolve. The idea is based on capturing discriminativeness and representativeness of each instance and eliminating the outliers. The final models are used to classify faces on novel datasets with possibly different characteristics. On benchmark datasets, our results are comparable to or better than state-of-the-art studies for the task of face identification.Comment: Draft version of the stud

Getting to Know Our Web Archive: A Pilot Project to Collaboratively Increase Access to Digital Cultural Heritage Materials in Wyoming

The University of Wyoming is the only four year higher education institution in the state, a unique position amongst colleges and universities in the United States. Given this unusual status it is especially important that the university libraries use their resources to identify and partner with communities around the state to build collections that preserve their cultural heritage. An Archive-It subscription was purchased in 2016, with an initial goal of capturing university related materials. In an effort to expand the scope and meaningfulness of the web archive, a project has been undertaken to use university and statewide relationships to build a Wyoming focused Native American digital cultural heritage collection comprised of web-based materials. This is an interdepartmental effort led by the Digital Collections Librarian and the Metadata Librarian that includes collaboration within the library, the university, and the state

Evaluating the development of wearable devices, personal data assistants and the use of other mobile devices in further and higher education institutions

This report presents technical evaluation and case studies of the use of wearable and mobile computing mobile devices in further and higher education. The first section provides technical evaluation of the current state of the art in wearable and mobile technologies and reviews several innovative wearable products that have been developed in recent years. The second section examines three scenarios for further and higher education where wearable and mobile devices are currently being used. The three scenarios include: (i) the delivery of lectures over mobile devices, (ii) the augmentation of the physical campus with a virtual and mobile component, and (iii) the use of PDAs and mobile devices in field studies. The first scenario explores the use of web lectures including an evaluation of IBM's Web Lecture Services and 3Com's learning assistant. The second scenario explores models for a campus without walls evaluating the Handsprings to Learning projects at East Carolina University and ActiveCampus at the University of California San Diego . The third scenario explores the use of wearable and mobile devices for field trips examining San Francisco Exploratorium's tool for capturing museum visits and the Cybertracker field computer. The third section of the report explores the uses and purposes for wearable and mobile devices in tertiary education, identifying key trends and issues to be considered when piloting the use of these devices in educational contexts

Finding Bugs in Web Applications Using Dynamic Test Generation and Explicit State Model Checking

Web script crashes and malformed dynamically-generated web pages are common errors, and they seriously impact the usability of web applications. Current tools for web-page validation cannot handle the dynamically generated pages that are ubiquitous on today's Internet. We present a dynamic test generation technique for the domain of dynamic web applications. The technique utilizes both combined concrete and symbolic execution and explicit-state model checking. The technique generates tests automatically, runs the tests capturing logical constraints on inputs, and minimizes the conditions on the inputs to failing tests, so that the resulting bug reports are small and useful in finding and fixing the underlying faults. Our tool Apollo implements the technique for the PHP programming language. Apollo generates test inputs for a web application, monitors the application for crashes, and validates that the output conforms to the HTML specification. This paper presents Apollo's algorithms and implementation, and an experimental evaluation that revealed 302 faults in 6 PHP web applications

Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes

In this work we propose approaches to effectively transfer knowledge from weakly labeled web audio data. We first describe a convolutional neural network (CNN) based framework for sound event detection and classification using weakly labeled audio data. Our model trains efficiently from audios of variable lengths; hence, it is well suited for transfer learning. We then propose methods to learn representations using this model which can be effectively used for solving the target task. We study both transductive and inductive transfer learning tasks, showing the effectiveness of our methods for both domain and task adaptation. We show that the learned representations using the proposed CNN model generalizes well enough to reach human level accuracy on ESC-50 sound events dataset and set state of art results on this dataset. We further use them for acoustic scene classification task and once again show that our proposed approaches suit well for this task as well. We also show that our methods are helpful in capturing semantic meanings and relations as well. Moreover, in this process we also set state-of-art results on Audioset dataset, relying on balanced training set.Comment: ICASSP 201

German compound splitting using the compound productivity of morphemes

In this work, we present a novel compound splitting method for German by capturing the compound productivity of morphemes. We use a giga web corpus to create a lexicon and decompose noun compounds by computing the probabilities of compound elements as bound and free morphemes. Furthermore, we provide a uniformed evaluation of several unsupervised approaches and morphological analysers for the task. Our method achieved a high F1 score of 0.92, which was a comparable result to state-of-the-art methods

Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning

Glaucoma is a major eye disease, leading to vision loss in the absence of proper medical treatment. Current diagnosis of glaucoma is performed by ophthalmologists who are often analyzing several types of medical images generated by different types of medical equipment. Capturing and analyzing these medical images is labor-intensive and expensive. In this paper, we present a novel computational approach towards glaucoma diagnosis and localization, only making use of eye fundus images that are analyzed by state-of-the-art deep learning techniques. Specifically, our approach leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) for glaucoma diagnosis and localization, respectively. Quantitative and qualitative results, as obtained for a small-sized dataset with no segmentation ground truth, demonstrate that the proposed approach is promising, for instance achieving an accuracy of 0.91$\pm0.02$ and an ROC-AUC score of 0.94 for the diagnosis task. Furthermore, we present a publicly available prototype web application that integrates our predictive model, with the goal of making effective glaucoma diagnosis available to a wide audience.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/010120

An IoT-enabled Framework for Context-aware Role-based Access Control

We present a framework for enforcing the application of context-aware Role-based Access Control policies based on an Internet of Things eco-system inspired by the Google\u2019s Physical Web. In this setting we are interested in capturing three contextual dimensions, namely who-where-when, and using these information to restrict access to shared resources. Formally, the framework consists of features types, an automata-based model of time-sensitive roles, context-aware permission rules, and an IoT infrastructure based on Eddystone Beacons for validating a policy against the current state of users