Computer Sciences Commons^™

Body-Oriented Gesture Generation System For Medical Interpreter Robots Based On Reinforcement Learning From Human Feedback, Tung Ngo, Emma Murphy, Conor Mcginn, Robert Ross

Conference papers

Medical interpreters are crucial in facilitating communication between healthcare stakeholders who speak different languages. Body-oriented gestures convey critical information essential for accurate and high-quality interpretation in healthcare settings. This study introduces a body-oriented gesture generation system designed for medical interpreter robots based on reinforcement learning from human feedback (RLHF). The system allows robots to interpret more naturally and improve over time through interactions with humans. By adopting a human-centered development approach, we tailor our system to address the actual needs of healthcare stakeholders.

An Evaluation Of Features Extracted From Facial Images In The Context Of Binary Age Classification, Malik Awais Khan, Aurelia Power, Peter Corcoran, Christina Thorpe

Conference Papers

Age verification via facial images is used to enhance security, ensure online child safety, and manage access control with many approaches using classification models. However, many existing classification approaches face generalisation challenges due to homogenous racial nature of datasets. Moreover, many approaches fail to explicitly address the predictive potential of extracted facial features. To address the lack of racial diversity we selected representative samples from four different benchmark datasets: UTK-Face, Fg-Net, Morph and All-Age-Faces. Subsequently, we examined the predictive potential of local, global and hybrid sets of facial features. We extracted local features using two types of Local Binary Pattern …

How Can A Cloud Computing It Framework Be Created And Applied Effectively In The Online Printing Industry?, Stefan Meissner

Dissertations

This research aims to design a cloud computing IT framework for the online printing industry based on a detailed literature review, the development of proof of concepts (PoC), and the conduction of a focus group. The framework can be adopted by the online printing industry or by vendors of print-specific applications to optimize their products for the online printing industry. The author has been working in the online printing process optimization and automation since 2007. During this time, he got deep insight into many industry-specific applications, their architectural design, and their challenges being used in the context of online printing. …

An Image Processing Approach For Real-Time Safety Assessment Of Autonomous Drone Delivery, Assem A. Abdelhak, Dan Moss, Alan Hicks, Susan Mckeever

Articles

The aim of producing self-driving drones has driven many researchers to automate various drone driving functions, such as take-off, navigation, and landing. However, despite the emergence of delivery as one of the most important uses of autonomous drones, there is still no automatic way to verify the safety of the delivery stage. One of the primary steps in the delivery operation is to ensure that the dropping zone is a safe area on arrival and during the dropping process. This paper proposes an image-processing-based classification approach for the delivery drone dropping process at a predefined destination. It employs live streaming …

Generalised Zero-Shot Learning For Action Recognition Fusing Text And Image Gans, Kaiqiang Huang, Susan Mckeever, Luis Miralles-Pechuán

Articles

Generalized Zero-Shot Action Recognition (GZSAR) is geared towards recognizing classes that the model has not been trained on, while still maintaining robust performance on the familiar, trained classes. This approach mitigates the need for an extensive amount of labeled training data and enhances the efficient utilization of available datasets. The main contribution of this paper is a novel approach for GZSAR that combines the power of two Generative Adversarial Networks (GANs). One GAN is responsible for generating embeddings from visual representations, while the other GAN focuses on generating embeddings from textual representations. These generated embeddings are fused, with the selection …

Exploring The Impact Of Signal Quality Enhancement On Heart Sound Classification Models, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Articles

Limited cardiology resources increase the urgency for automated heart disease screening for the general public. Heart sound diagnostic models have been recently employed as a cost-effective solution for the initial screening of heart disease. Noise in heart sound recordings, however, can reduce the performance of such data-driven models. Various quality enhancement approaches have been adopted to alleviate the destructive impact of noise on model performance. One approach is universal noise reduction which applies denoising techniques to recordings, irrespective of their noise level. The second approach is targeted noise reduction, which applies denoising solely to recordings deemed to need it, based …

Optimization Of Biomedical Imaging Filters For Use In Recaptured Identity Document Classification, John Magee, Stephen Sheridan Phd, Christina Thorpe Phd

Conference papers

As banks and online financial institutions move toward full remote onboarding services, the attack vectors for bad actors increases to include those of recaptured identity documents. This type of fraud opens banking customers to potential crimes of identity theft, as well as causing reputational damage to the institutions involved. In this paper we extend existing research focusing on the use of biomedical imaging filters and their usefulness when classifying recaptured identity documents. We perform a grid search and demonstrate that different filter configurations exist that dramatically reduce the classification error rates compared to those achieved using only the default filter …

Exploring Wav2vec 2.0 Model For Heart Murmur Detection, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Conference papers

The lack of access to cardiology resources in many regions of the world has motivated the development of automatic diagnostic systems based on cardiac signals. In recent years, a wide range of supervised learning models have been proposed that can make an initial diagnosis of heart disease from heart sounds. To achieve high accuracy, however, such supervised learning models generally require a large amount of labeled data, which can be costly to obtain. In this regard, self-supervised learning has been recently employed to reduce the over-reliance on annotated data. Wav2vec 2.0 is an audio self-supervised learning model that has shown …

Sigite'23 Poster And Extended Abstract: The Proof Of Gold Is Fire: Measuring Stress To Show Impact Of Gender Based Initiatives In Computing Education, Alina Berry, Sarah Jane Delany

Other resources

Many initiatives across the world try to address the issue of gender imbalance in computing education. Evaluation of the impact of these initiatives is not always straightforward. Some interventions are able to demonstrate impact based on recruitment and retention numbers of underrepresented gender groups, in particular, women, often requiring a longer-term study to see impact. Others, especially shorter term interventions and those taking place during the teaching and learning process, frequently use feedback and other instruments as a measure of impact. This work reviews existing evaluation methods from the literature, categorises them as using statistical data, feedback or instruments, grouping …

A Deep Learning-Based Object Detection Framework For Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images, Ibrahim Hassan Syed, Susan Mckeever Dr., Kieran Feighan, David Power, Dympna O'Sullivan

Conference papers

Road maintenance and the early detection of road defects rely on routine pavement inspections. While advanced 3D laser profiling systems have the capability to automatically identify certain types of distress such as cracks and ruts, more complex pavement damage, including patches, often require manual identification. To address this limitation, this study proposes an automated patch detection system that employs object detection techniques. The results demonstrate the ability of object detection models to accurately identify patches in laser profiling images, indicating that the proposed approach has the capability to significantly enhance automation in visual inspection processes. This has the potential for …

An Investigation Into The Application Of The Meijering Filter For Document Recapture Detection, John Magee, Stephen Sheridan Phd, Christina Thorpe Phd

Conference papers

The proliferation of mobile devices allows financial institutions to offer remote customer services, such as remote account opening. Manipulation of identity documents using image processing software is a low-cost, high-risk threat to modern financial systems, opening these institutions to fraud through crimes related to identity theft. In this paper we describe our exploratory research into the application of biomedical image algorithms to the domain of document recapture detection. We perform a statistical analysis to compare different types of recaptured documents and train a support vector machine classifier on the raw histogram data generated using the Meijering filter. The results show …

A Method For Generating A Non-Manual Feature Model For Sign Language Processing, Robert G. Smith Dr, Markus Hofmann Dr

Articles

While recent approaches to sign language processing have shifted to the domain of Machine Learning (ML), the treatment of Non-Manual Features (NMFs) remains an open question. The principal challenge facing this method is the comparatively small sign language corpora available for training machine learning models. This study produces a statistical model which may be used in future ML, rules-based, and hybrid-learning approaches for sign language processing tasks. In doing so, this research explores the emerging patterns of non-manual articulation concerning grammatical classes in Irish Sign Language (ISL). The experimental method applied here is a novel implementation of an association rules …

Interpretable Timbre Synthesis Using Variational Autoencoders Regularized On Timbre Descriptors, Anastasia Natsiou, Luca Longo, Sean O'Leary

Conference papers

Controllable timbre synthesis has been a subject of research for several decades, and deep neural networks have been the most successful in this area. Deep generative models such as Variational Autoencoders (VAEs) have the ability to generate a high-level representation of audio while providing a structured latent space. Despite their advantages, the interpretability of these latent spaces in terms of human perception is often limited. To address this limitation and enhance the control over timbre generation, we propose a regularized VAE-based latent space that incorporates timbre descriptors. Moreover, we suggest a more concise representation of sound by utilizing its harmonic …

Exploring Mental Models For Explainable Artificial Intelligence: Engaging Cross-Disciplinary Teams Using A Design Thinking Approach, Helen Sheridan, Emma Murphy Dr., Dympna O'Sullivan Dr.

Conference papers

Exploring end-users’ understanding of Artificial Intelligence (AI) systems’ behaviours and outputs is crucial in developing accessible Explainable Artificial Intelligence (XAI) solutions. Investigating mental models of AI systems is core in understanding and explaining the often opaque, complex, and unpredictable nature of AI. Researchers engage surveys, interviews, and observations for software systems, yielding useful evaluations. However, an evaluation gulf still exists, primarily around comprehending end-users' understanding of AI systems It has been argued that by exploring theories related to human decision-making examining the fields of psychology, philosophy, and human computer interaction (HCI) in a more people-centric rather than product or technology-centric …

Poly-Gan: Regularizing Polygons With Generative Adversarial Networks, Lasith Niroshan, James Carswell

Conference Papers

Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a data-driven and Deep Learning (DL) based approach for regularizing OpenStreetMap building polygon edges. The study introduces a building footprint regularization technique (Poly-GAN) that utilises a Generative Adversarial Network model trained on irregular building footprints and OSM vector data. The proposed method is particularly relevant for map features …

Exploiting Association Rules Mining To Inform The Use Of Non-Manual Features In Sign Language Processing, Robert G. Smith

Other Resources

In recent years, the use of virtual assistants and voice user interfaces has become a latent part of modern living. Unseen to the user are the various artificial intelligence and natural language processing technologies, the vast datasets, and the linguistic insights that underpin such tools. The technologies supporting them have chiefly targeted widely used spoken languages, leaving sign language users at a disadvantage. One important reason why sign languages are unsupported by such tools is a requirement of the underpinning technologies for a comprehensive description of the language. Sign language processing technologies endeavour to bridge this technology inequality.

Recent approaches …

An Exploration Of The Latent Space Of A Convolutional Variational Autoencoder For The Generation Of Musical Instrument Tones, Anastasia Natsiou, Sean O'Leary, Luca Longo

Conference papers

Variational Autoencoders (VAEs) constitute one of the most significant deep generative models for the creation of synthetic samples. In the field of audio synthesis, VAEs have been widely used for the generation of natural and expressive sounds, such as music or speech. However, VAEs are often considered black boxes and the attributes that contribute to the synthesis of a sound are yet unsolved. Existing research focused on the way input data can influence the generation of latent space, and how this latent space can create synthetic data, is still insufficient. In this manuscript, we investigate the interpretability of the latent …

Co-Designing Assistive Technology With And For Persons Living With Dementia, Dympna O'Sullivan, Jonathan Turner, Siobhan O'Neill, Micheal Wilson, Julie Doyle

Conference papers

Dementia is a chronic and progressive neurodegenerative illness, which can lead to significant difficulties in a person’s capacity to perform activities of daily living (ADLs) and engage in meaningful activities. There is an acute need, which digital health technologies can potentially fulfil, to provide proactive support for persons living with dementia (PLwD) and their caregivers. However, there is limited involvement of PLwD in the design of technology that could be used to support their personal plans for independent living at home. In this paper, we describe how we are employing a co-design methodology to support engagement in an assistive technology …

Wifi-Based Human Activity Recognition Using Attention-Based Bilstm, Amany Elkelany, Robert J. Ross, Susan Mckeever

Conference papers

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the …

Applying Positional Encoding To Enhance Vision-Language Transformers, Xuehao Liu, Sarah Jane Delany, Susan Mckeever

Conference papers

Positional encoding is used in both natural language and computer vision transformers. It provides information on sequence order and relative position of input tokens (such as of words in a sentence) for higher performance. Unlike the pure language and vision transformers, vision-language transformers do not currently exploit positional encoding schemes to enrich input information. We show that capturing location information of visual features can help vision-language transformers improve their performance. We take Oscar, one of the state-of-the-art (SOTA) vision-language transformers as an example transformer for implanting positional encoding. We use image captioning as a downstream task to test performance. We …

Fair4pghd: A Framework For Fair Implementation Over Pghd, Abdullahi Abubakar Kawu, Dympna O'Sullivan, Lucy Hederman, Mirjam Van Reisen

Articles

Patient Generated Health Data (PGHD) are being considered for integration with health facilities, however little is known about how such data can be made machine-actionable in a way that meets FAIR guidelines. This article proposes a 5-stage framework that can be used to achieve this.

Determining The Proportionality Of Ischemic Stroke Risk Factors To Age, Elizabeth Hunter, John D. Kelleher

Articles

While age is an important risk factor, there are some disadvantages to including it in a stroke risk model: age can dominate the risk score and lead to over-or under-predictions in some age groups. There is evidence to suggest that some of these disadvantages are due to the non-proportionality of other risk factors with age, eg, risk factors contribute differently to stroke risk based on an individual’s age. In this paper, we present a framework to test if risk factors are proportional with age. We then apply the framework to a set of risk factors using Framingham heart study data …

Survey On Outdoor Navigation Applications For People With Visual Impairment, Fatmaelzahraa Eltaher, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever

Datasets

Outdoor navigation is a very challenging activity for People who suffer from Blindness or Visually Impairment (PBVI). Having examined the current literature, we conclude that there are very few publications providing a nuanced understanding of how PBVI undertake a journey in an outdoor environment and what their main challenges and obstacles are. To throw some light on this gap, we conducted a questionnaire in collaboration with the National Council for the Blind Ireland (NCBI) for 49 PBVI. Our questionnaire gathers information about key aspects related to PBVI outdoor navigation such as support tools/devices, hazards, journey preparation, crossing roads, and understanding …

Towards A Framework For Privacy-Preserving Pedestrian Analysis, Anil Kunchala, Mélanie Bouroche, Bianca Schoen-Phelan

Conference papers

The design of pedestrian-friendly infrastructures plays a crucial role in creating sustainable transportation in urban environments. Analyzing pedestrian behaviour in response to existing infrastructure is pivotal to planning, maintaining, and creating more pedestrian-friendly facilities. Many approaches have been proposed to extract such behaviour by applying deep learning models to video data. Video data, however, includes an broad spectrum of privacy-sensitive information about individuals, such as their location at a given time or who they are with. Most of the existing models use privacy-invasive methodologies to track, detect, and analyse individual or group pedestrian behaviour patterns. As a step towards privacy-preserving …

Identifying Gendered Language, Shweta Soundararajan, Sarah Jane Delany

Academic Posters Collection

Gendered language refers to the use of words that indicate the gender of an individual. It can be explicit, where the gender is directly implied by the specific words used (e.g., mother, she, man), or it can be implicit, where societal roles and behaviors convey a person's gender. For example, expectations that women display communal traits (e.g., affectionate, caring, gentle) and men display agentic traits (e.g., assertive, competitive, decisive). The presence of gendered language in natural language processing (NLP) systems can reinforce gender stereotypes and bias. Our work introduces an approach to creating gendered language datasets using ChatGPT. These datasets …

Improve Engagement With Full Labs And Motivated Students: Interactive Labs Via Low Stakes Assessment, Susan Mckeever, Patricia O'Byrne, Amanda O'Farrell

Academic Posters Collection

Poor engagement and attendance is an endemic problem at third level, particularly post covid. Our approach shows how the use of regular in-lab assessment and challenges can dramatically increase student participation and learning. Using three case studies, we demonstrate how we have successfully used this low-stakes assessment approach to improve student outcomes, across a range of modules.

Detecting Patches On Road Pavement Images Acquired With 3d Laser Sensors Using Object Detection And Deep Learning, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever, David Power, Ray Mcgowan, Kieran Feighan

Academic Posters Collection

Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This work proposes an automatic patch detection system using an object detection technique. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems.

Towards Accommodating Gerunds Within The Sign Language Lexicon, Zaid Mohammed, Irene Murtagh

Academic Posters Collection

This work is part of ongoing research work that focuses on the linguistic analysis and computational description of five different Sign Languages (SLs) namely Irish Sign Language (ISL), Flemish Sign Language (VGT), Dutch Sign Language (NGT), Spanish Sign Language (LSE), and British Sign Language (BSL) as part of the SignON project. This work will be leveraged to inform the development of SL lexicon entries for a Sign Language Machine Translation (SLMT) system. In particular, this research focuses on ISL. We investigate the existence of constructions similar to or equivalent in functionality to gerunds in spoken language, in particular, English. The …

The European Commission And Ai: Guidelines, Acts And Plans Impacting The Teaching Of Ai And Teaching With Ai, Keith Quille, Brett A. Becker, Lidia Vidal-Meliá

Academic Posters Collection

Recent developments, guidelines, and acts by the European Commission have started to frame policy for AI and related areas such as ML and data, not only for the broader community, but in the context of education specifically. This poster presents a succinct overview of these developments. Specifically, we look to bring together all publications that might impact the teaching of AI (for example, teacher expectations in the coming years around AI competencies) and publications that affect the use of AI in the classroom. We mean using tools and systems that incorporate both ‘Good Old Fashioned’ AI and those that can …

The Proof Is In The Pudding – Using Perceived Stress To Measure Short-Term Impact In Initiatives To Enhance Gender Balance In Computing Education, Alina Berry, Sarah Jane Delany

Academic Posters Collection

The problem of gender imbalance in computing higher education has forced academics and professionals to implement a wide range of initiatives. Many initiatives use recruitment or retention numbers as their most obvious evidence of impact. This type of evidence of impact is, however, more resource heavy to obtain, as well as often requires a longitudinal approach. There are many shorter term initiatives that use other ways to measure their success.

First, this poster presents with a review of existing evaluation measures in interventions to recruit and retain women in computing education across the board. Three main groups of evaluation come …