A Framework for the Development of a Dynamic Adaptive Intelligent User Interface to Enhance the User Experience

Johnston, Vivien; Black, Michaela; Wallace, Jonathan; Mulvenna, Maurice; Bond, Raymond

doi:10.1145/3335082.3335125

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…Johnston et al [10] identified the standardised process for the creation of IUIs as an under-researched area. The work aims to improve user experience universally through provision of a framework for the development of IUIs.…”

Section: Related Workmentioning

confidence: 99%

Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

et al. 2021

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It is becoming increasingly apparent that a significant amount of the population suffers from mental health problems, such as stress, depression, and anxiety. These issues are a result of a vast range of factors, such as genetic conditions, social circumstances, and lifestyle influences. A key cause, or contributor, for many people is their work; poor mental state can be exacerbated by jobs and a person’s working environment. Additionally, as the information age continues to burgeon, people are increasingly sedentary in their working lives, spending more of their days seated, and less time moving around. It is a well-known fact that a decrease in physical activity is detrimental to mental well-being. Therefore, the need for innovative research and development to combat negativity early is required. Implementing solutions using Artificial Intelligence has great potential in this field of research. This work proposes a solution to this problem domain, utilising two concepts of Artificial Intelligence, namely, Convolutional Neural Networks and Generative Adversarial Networks. A CNN is trained to accurately predict when an individual is experiencing negative emotions, achieving a top accuracy of 80.38% with a loss of 0.42. A GAN is trained to synthesise images from an input domain that can be attributed to evoking position emotions. A Graphical User Interface is created to display the generated media to users in order to boost mood and reduce feelings of stress. The work demonstrates the capability for using Deep Learning to identify stress and negative mood, and the strategies that can be implemented to reduce them.

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Section: Related Workmentioning

confidence: 99%

Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

et al. 2021

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“…greater focus, reduced distraction and attenuated anxiety). These appear in Figure 1 and include dynamic, adaptive and intelligent interfacesof which-research has identified an intersectional gap in their interactivity (Johnston et al, 2019). Specifically, dynamic systems understand the user, their device and their environment to provision a basic user experience.…”

Section: Figure 1: Engineering Components Of Adaptive Systemsmentioning

confidence: 99%

“…While many contemporary CNNs learn hierarchical descriptors from input stimuli and then describe input data into smaller, highly abstract representations, SensorAble-like those studies from Barros, Johnston et al, (2017;2019)-does not use a strict classification technique. Rather than learning tiered contours and shapes used in general image recognition tasks, convolutional units instead train on sensorial and spatial information sometimes originally existing as images and other times transformed into visual data (Speck et al, 2016).…”

Section: Figure 3: Combined Multi-sensory Inputs (Across Three Domains) May Provide Greater Accuracymentioning

confidence: 99%

“…In modelling this assembly and managing both environmental and physiological stimuli using engineering principles, my research aspires to deep learning models that sense, categorize and alert autistic individuals of ecological distractions, biophysical cues and other multimodal input that-left unchecked-could decrease individual focus and increase distractibility and anxiety. The designs that follow are based upon valid and reliable constructs presented in recent, peripherally related research, including: (i) a framework for developing adaptive intelligent user interfaces that enhances user experience (Johnston et al, 2019); and, (ii) convolution neural networks (CNNs) that improve expression recognition through emotionmodulated attention (Barros et al, 2017). My intention is to weave a compelling and explicit rationale as to how and why deep learning models make the most sense when learning tasks derived from image, time-series and textdata and applying these to the SensorAble Research Project.…”

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confidence: 99%

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In Defence of ML/CNN for the SensorAble Research Project

Ruttenberg¹

2020

Preprint

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Animals and humans use a midbrain structure to coordinate and process relevant visual and auditory stimuli while suppressing distracting information. In modelling this assembly and managing both environmental and physiological stimuli using engineering principles, my research aspires to deep learning models that sense, categorize and alert autistic individuals of ecological distractions, biophysical cues and other multimodal input that—left unchecked—could decrease individual focus and increase distractibility and anxiety. The designs that follow are based upon valid and reliable constructs presented in recent, peripherally related research, including: (i) a framework for developing adaptive intelligent user interfaces that enhances user experience (Johnston et al., 2019); and, (ii) convolution neural networks (CNNs) that improve expression recognition through emotion- modulated attention (Barros et al., 2017). My intention is to weave a compelling and explicit rationale as to how and why deep learning models make the most sense when learning tasks derived from image, time-series and text- data and applying these to the SensorAble Research Project.

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“…The third related research paradigm named dynamic taxonomies, prunes the taxonomy according to the informal keyword given manually by the user, e.g. in [15,24,29,31,50,56,58]. Using dynamic taxonomies, the user can most directly interact with the online portal, but the domain is semantically reduced according to the keyword.…”

Section: Introductionmentioning

confidence: 99%

TaxoMulti: Rule-Based Expert System to Customize Product Taxonomies for Multi-Channel E-commerce

Angermann

2022

SN COMPUT. SCI.

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Taxonomies are used as product categories to facilitate users navigating through an e-commerce portal with the help of hierarchically structured concepts. However, the identical taxonomy is shown to each customer regardless of the channel used. This is challenging for the customers in terms of user experience, as the screen size is rigid, and has not a flexible format like a printed catalog. Simply reducing the taxonomy as suggested in existing works is not sufficient, as it leads to semantic misrepresentation of the product domain. To overcome the inflexibility of product taxonomies, the rule-based expert system TaxoMulti is presented in this paper. The main objectives of our descriptive research are the formulation of the taxonomy over- and undersize problem in multi-channel context, before different types of flexible mediator concepts are discussed that allow overcoming these challenges. Using our novel method, marketing experts can now provide different taxonomies including the same semantics to be shown on different channels. The method is implemented using logic programming, allowing the integration of an inference engine utilizing background knowledge without changing the underlying logic of the used information (management) system. The comprehensive experiments on three public and private databases highlight the improvement when adding different types of mediator concepts for the adaption process. Compared to existing best performing works in related fields, TaxoMulti has achieved an improvement of + 26.31 % for the reduction of the taxonomy, + 60 % for the enlargement of the taxonomy, and + 21.21 % in terms of flexibility.

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A Framework for the Development of a Dynamic Adaptive Intelligent User Interface to Enhance the User Experience

Cited by 11 publications

References 9 publications

Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

Realtime Emotional Reflective User Interface Based on Deep Convolutional Neural Networks and Generative Adversarial Networks

In Defence of ML/CNN for the SensorAble Research Project

TaxoMulti: Rule-Based Expert System to Customize Product Taxonomies for Multi-Channel E-commerce

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