Audrey Mbogho | University of Cape Town (original) (raw)

Papers by Audrey Mbogho

Learning activities with tangible user interfaces provide the benefits of active and peer mediate... more Learning activities with tangible user interfaces provide the benefits of active and peer mediated learning, while offering assistance from an autonomous guide on the side. Yet tangible user interfaces must typically be custom developed by computer scientists, and only rarely is the assistance of teachers sought. We present a new tool that gives teachers the power to create their own educational applications with tangible user interfaces. Using actual scientific specimens, the teachers can define object attributes for the students to sort on, and also develop curriculum-appropriate hints. We describe our computer vision-based approach, which enables recognition of tags representing dichotomous keys defined by a teacher. Teachers use our back-end system to define the dichotomous keys and other parameters for the learning activities, while our front-end system uses those parameters to guide the students.

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INTED2017 Proceedings, 2017

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2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016

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Proceedings of the First African Conference on Human Computer Interaction - AfriCHI'16, 2016

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Procedia Computer Science, 2016

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Smart Innovation, Systems and Technologies, 2016

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Proceedings of the Seventh International Conference on Information and Communication Technologies and Development - ICTD '15, 2015

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2013 12th International Conference on Machine Learning and Applications, 2013

ABSTRACT Recording university lectures through lecture capture systems is increasingly common, ge... more ABSTRACT Recording university lectures through lecture capture systems is increasingly common, generating large amounts of audio and video data. Transcribing recording s greatly enhances their usefulness by making them easy to search. However, the number of recordings accumulates rapidly, rendering manual transcription impractical. Automatic transcription, on the other hand, suffers from low levels of accuracy, partly due to the special language of academic disciplines, which standard language models do not cover. This paper looks into the use of Wikipedia to dynamically adapt language models for scholarly speech. We propose Ranked Word Correct Rate as a new metric better aligned with the goals of improving transcript searchability and specialist word recognition. The study shows that, while overall transcription accuracy may remain low, targeted language modeling can substantially improve searchability, an important goal in its own right.

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Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07, 2007

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Communications in Computer and Information Science, 2014

ABSTRACT Feature selection is an important data pre-processing step that comes before applying a ... more ABSTRACT Feature selection is an important data pre-processing step that comes before applying a machine learning algorithm. It removes irrelevant and redundant attributes from the dataset with an aim of improving the algorithm performance. There exist feature selection methods which focus on discovering features that are most suitable. These methods include wrappers, a subroutine of the learning algorithm itself, and filters, which discover features according to heuristics, based on the data characteristics and not tied to a specific algorithm. This paper improves the filter approach by enabling it to select strongly relevant and weakly relevant features and gives room to the researcher to decide which of the weakly relevant features to include. This new approach brings clarity and understandability to the feature selection preprocessing step.

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"A dissertation submitted to the Graduate Faculty in Philosphy ... " Thesis (Ph. D.) --... more "A dissertation submitted to the Graduate Faculty in Philosphy ... " Thesis (Ph. D.) -- City University of New York, 2006. Includes bibliographical references (leaves 102-108).

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A major obstacle in the development of computer vision-based interfaces is the uncertainty in the... more A major obstacle in the development of computer vision-based interfaces is the uncertainty in the image data. Guaranteeing reliable program behavior while the inputs cannot be relied upon to take any specific values is desirable but extremely challenging. We have experimented with various strategies for addressing this problem in a controlled environment and have identified some that we find promising. Using visual tags, we encode object attributes, capture them with a camera, and read them. The test applications are implemented as Macromedia Director movies with Lingo scripting, while the image analysis module is implemented in C++ as a Lingo Xtra. We conclude that evaluation through rigorous experimentation and testing is a suitable approach for discovering techniques that work well and the conditions that maximize the chances for optimal performance.

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