Physical Sciences and Mathematics Commons^™

Using Chatgpt To Generate Gendered Language, Shweta Soundararajan, Manuela Nayantara Jeyaraj, Sarah Jane Delany

Conference papers

Gendered language is the use of words that denote an individual's gender. This can be explicit where the gender is evident in the actual word used, e.g. mother, she, man, but it can also be implicit where social roles or behaviours can signal an individual'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 use of gendered language in NLP systems can perpetuate gender stereotypes and bias. This paper proposes an approach to generating gendered language datasets using ChatGPT which will provide data for data-driven …

Spectralomics – Towards A Holistic Adaptation Of Label Free Spectroscopy, Hugh Byrne

Articles

Vibrational spectroscopy, largely based on infrared absorption and Raman scattering techniques, is much vaunted as a label free approach, delivering a high content, holistic characterisation of a sample, with demonstrable applications in a broad range of fields, from process analytical technologies and preclinical drug screening, to disease diagnostics, therapeutics, prognostics and personalised medicine. However, in the analysis of such complex systems, a trend has emerged in which spectral analysis is reduced to the identification of individual peaks, based on reference tables of assignments derived from literature, which are then interpreted as biomarkers. More sophisticated analysis attempts to unmix the spectrum …

Exploring The Design Of Low-End Technology To Increase Patient Connectivity To Electronic Health Records, Rens Kievit, Abdullahi Abubakar Kawu, Mirjam Van Reisen, Dympna O'Sullivan, Lucy Hederman

Conference papers

The tracking of the vitals of patients with long term health problems is essential for clinicians to determine proper care. Using Patient Generated Health Data (PGHD) communicated remotely allows patients to be monitored without requiring frequent hospital visits. Issues might arise when the communication of data digitally is difficult or impossible due to a lack of access to internet or a low level of digital literacy as is the case in many African countries. The VODAN-Africa project (van Reisen et al., 2021) started in 2020 and has greatly increased the capabilities of clinics in different countries in both Africa and …

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 …

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 …

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 …

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 …

A Lak Of Direction Misalignment Between The Goals Of Learning Analytics And Its Research Scholarship, Benjamin A. Motz, Yoav Bergner, Christopher A. Brooks, Anna Gladden, Geraldine Gray, Charles Lang, Warren Li, Fernando Marmolejo-Ramos, Joshua D. Quick

Articles

Learning analytics defines itself with a focus on data from learners and learning environments, with corresponding goals of understanding and optimizing student learning. In this regard, learning analytics research, ideally, should be characterized by studies that make use of data from learners engaged in education systems, should measure student learning, and should make efforts to intervene and improve these learning environments.

Towards An Improved Understanding Of The Concept Of Style And Its Implications For Textual Style Transfer, Somayeh Jafaritazehjani

Doctoral

The concept of linguistic style denotes that many aspects of text can vary while maintaining a same source core semantic meaning. For example, a message may be written in a formal or informal style. The textual style transfer problem aims at generating a paraphrase of a given text by modifying its style while preserving its content. To the best of our knowledge, within the literature on textual style transfer, there is no standard widely accepted definition of the concept of style. Moreover, very few works have investigated the characteristics of language styles. Therefore, previous research, as far as our knowledge …

Use Of Machine Learning Methods In Automatic Assessment Programming Assignments, Botond Tarcsay

Masters

Programming has become an important skill in today’s world and is taught widely both in traditional settings and online. Instructors need to assess increasing amounts of student work. Unit testing can contribute to the automation of the grading process; however, it cannot assess the structures, style and partially correct source code or differentiate between levels of achievement. The topic of this thesis is an investigation into the use of machine learning methods for assessing the correctness and quality of code, with the ultimate goal of assisting instructors in the grading process. In this research, we have used nine different machine …

Exploring The Integration Of Patient Generated Health Data In A Fair Digital Health System In Low-Resourced Settings: A User-Centered Approach, Abdullahi Abubakar Kawu, Rens Kievit, Adamu Abubakar, Mirjam Van Reisen, Dympna O'Sullivan, Lucy Hederman

Conference papers

This article presents the initial user-centered research exploring the opportunities in the collection of Patient-Generated Health Data (PGHD) within the context of a project aimed at improving health management and outcomes among residents in African countries. Through interviews with a doctor, a patient and two data managers, the local status and opinions regarding PGHD collection, integration and use are investigated. The findings suggest that PGHD have only been encountered in paper forms - and are mostly patient driven, however opportunities for PGHD for the facility and patient were identified and included supporting the treatment of whitecollar hypertension, treatment planning and …

Synthetic Heart Sound Dataset, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Datasets

The repository contains synthetic heart sound recordings. The publication related to this dataset is "Exploring the impact of noise and degradations on heart sound classification models", Biomedical Signal Processing and Control journal.

Dataset For Gendered Language, Shweta Soundararajan

Datasets

Gendered language is the use of words that denote an individual’s gender. This can be explicit where the gender is evident in the actual word used, e.g. mother, she, man, but it can also be implicit where social roles or behaviours can signal an individual’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 use of gendered language in NLP systems can perpetuate gender stereotypes and bias. This paper proposes an approach to generating gendered language datasets using ChatGPT which will provide data for data-driven …

Structured Dialogue State Management For Task-Oriented Dialogue Systems, Anh Duong Trinh

Doctoral

Human-machine conversational agents have developed at a rapid pace in recent years, bolstered through the application of advanced technologies such as deep learning. Today, dialogue systems are useful in assisting users in various activities, especially task-oriented dialogue systems in specific dialogue domains. However, they continue to be limited in many ways. Arguably the biggest challenge lies in the complexity of natural language and interpersonal communication, and the lack of human context and knowledge available to these systems. This leads to the question of whether dialogue systems, and in particular task-oriented dialogue systems, can be enhanced to leverage various language properties. …

Determining Child Sexual Abuse Posts Based On Artificial Intelligence, Susan Mckeever, Christina Thorpe, Vuong Ngo

Conference papers

The volume of child sexual abuse materials (CSAM) created and shared daily both surface web platforms such as Twitter and dark web forums is very high. Based on volume, it is not viable for human experts to intercept or identify CSAM manually. However, automatically detecting and analysing child sexual abusive language in online text is challenging and time-intensive, mostly due to the variety of data formats and privacy constraints of hosting platforms. We propose a CSAM detection intelligence algorithm based on natural language processing and machine learning techniques. Our CSAM detection model is not only used to remove CSAM on …

The Potential And Limitations Of Conversational Agents For Chronic Conditions And Well-Being, Ekaterina Uetova, Lucy Hederman, Robert J. Ross, Dympna O'Sullivan

Articles

Conversational agents are becoming more common in the health and wellness domains in part due to assumptions regarding potential improvements in individuals’ outcomes. This paper presents initial findings from a review of conversational agent use in healthcare for chronic conditions and well-being. A search of the literature was performed on electronic databases PubMed, ACM Digital Library, Scopus and IEEE Xplore. Studies were included if they were focused on chronic disorder management, disease prevention or lifestyle change and if systems were tested on target user groups. This paper investigates the health domains, the user profiles and reasons why conversational agents may …