Faculty of Design and Creative Technologies (Te Ara Auaha)
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The Faculty of Design and Creative Technologies (Te Ara Auaha) is comprised of four school; Colab, the School of Art and Design, the School of Communication Studies and the School of Engineering, Computer and Mathematical Sciences. It also has Institutes, Centres and Labs across the Arts and Sciences in a mix that blends the traditional and the new, praxis and theory.
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Browsing Faculty of Design and Creative Technologies (Te Ara Auaha) by Author "Adeleye, Olayinka"
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- ItemAndroCon: An Android-Based Context-Aware Middleware Framework(European Alliance for Innovation n.o., 2018-03-14) Yu, Jian; Z. Sheng, Quan; Adeleye, Olayinka; Wang, ChrisMobile devices have become major sources of context-aware data due to their ubiquity and sensing capabilities. However, deploying mobile devices as dynamic, unabridged context data provider either locally or remotely is challenging, due to their limited computing capabilities. Moreover, mobile sensors are limited to physical context data acquisition and there is a need to integrate physical data provided by these sensors with social context data provided by various mobile applications. Such data integration is necessary in order to have a robust data sources for various context-aware applications. In this paper, we present AndroCon, an Android-based, context-aware middleware framework that enables mobile devices to acquire, integrate, manage context data and to provision the data to applications both locally and remotely. AndroCon enables integration of both raw physical and social related context data. Instances of AndroCon have been achieved by interpreting and storing high-level context knowledge locally and utilizing web service technologies for data provisioning. We perform extensive experiments using AndroCon to collect, provision and manage both social and physical context data from di erent sources. We have also analyzed AndroCon’s performances based on its power consumption and CPU utilization.
- ItemSpeech Emotion Recognition Using Machine Learning — A Systematic Review(Elsevier BV, 2023-08-14) Madanian, Samaneh; Chen, Talen; Adeleye, Olayinka; Templeton, John Michael; Poellabauer, Christian; Parry, Dave; Schneider, Sandra LSpeech emotion recognition (SER) as a Machine Learning (ML) problem continues to garner a significant amount of research interest, especially in the affective computing domain. This is due to its increasing potential, algorithmic advancements, and applications in real-world scenarios. Human speech contains para-linguistic information that can be represented using quantitative features such as pitch, intensity, and Mel-Frequency Cepstral Coefficients (MFCC). SER is commonly achieved following three key steps: data processing, feature selection/extraction, and classification based on the underlying emotional features. The nature of these steps, coupled with the distinct features of human speech, underpin the use of ML methods for SER implementation. Recent research works in affective computing employed various ML methods for SER tasks; however, only a few of them capture the underlying techniques and methods that can be used to facilitate the three core steps of SER implementation. In addition, the challenges associated with these steps, and the state-of-the-art approaches used in tackling them are either ignored or sparsely discussed in these works. In this paper, we present a systematic review of research that addressed SER tasks from ML perspectives over the last decade, with emphasis on the three SER implementation steps. Different challenges, including the issue of low-classification-accuracy of Speaker-Independent experiments, and solutions associated with them, are discussed in detail. The review also provides guidelines for SER evaluation with a focus on common baselines, and metrics available for experimentation. This paper is expected to serve as a comprehensive guideline for SER researchers to design SER solutions using ML techniques, motivate possible improvements of existing SER models, or trigger novel techniques to enhance SER performance.