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Articles 1 - 30 of 75
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Data: The Good, The Bad And The Ethical, John D. Kelleher, Filipe Cabral Pinto, Luis M. Cortesao
Data: The Good, The Bad And The Ethical, John D. Kelleher, Filipe Cabral Pinto, Luis M. Cortesao
Articles
It is often the case with new technologies that it is very hard to predict their long-term impacts and as a result, although new technology may be beneficial in the short term, it can still cause problems in the longer term. This is what happened with oil by-products in different areas: the use of plastic as a disposable material did not take into account the hundreds of years necessary for its decomposition and its related long-term environmental damage. Data is said to be the new oil. The message to be conveyed is associated with its intrinsic value. But as in …
Lightgwas: A Novel Machine Learning Procedure For Genome-Wide Association Study, Ambrozio Bruno, Luca Longo, Lucas Rizzo
Lightgwas: A Novel Machine Learning Procedure For Genome-Wide Association Study, Ambrozio Bruno, Luca Longo, Lucas Rizzo
Articles
This paper proposes a novel machine learning procedure for genome-wide association study (GWAS), named LightGWAS. It is based on the LightGBM framework, in addition to being a single, resilient, autonomous and scalable solution to address common limitations of GWAS implementations found in the literature. These include reliance on massive manual quality control steps and specific GWAS methods for each type of dataset morphology and size. Through this research, LightGWAS has been contrasted against PLINK2, one of the current state-of-the-art for GWAS implementations based on general linear model with support to firth regularisation. The mean differences measured upon standard classification metrics, …
Exploring The Potential Of Defeasible Argumentation For Quantitative Inferences In Real-World Contexts: An Assessment Of Computational Trust, Lucas Rizzo, Pierpaolo Dondio, Luca Longo
Exploring The Potential Of Defeasible Argumentation For Quantitative Inferences In Real-World Contexts: An Assessment Of Computational Trust, Lucas Rizzo, Pierpaolo Dondio, Luca Longo
Articles
Argumentation has recently shown appealing properties for inference under uncertainty and conflicting knowledge. However, there is a lack of studies focused on the examination of its capacity of exploiting real-world knowledge bases for performing quantitative, case-by-case inferences. This study performs an analysis of the inferential capacity of a set of argument-based models, designed by a human reasoner, for the problem of trust assessment. Precisely, these models are exploited using data from Wikipedia, and are aimed at inferring the trustworthiness of its editors. A comparison against non-deductive approaches revealed that these models were superior according to values inferred to recognised trustworthy …
A Comparative Analysis Of Rule-Based, Model-Agnostic Methods For Explainable Artificial Intelligence, Giulia Vilone, Lucas Rizzo, Luca Longo
A Comparative Analysis Of Rule-Based, Model-Agnostic Methods For Explainable Artificial Intelligence, Giulia Vilone, Lucas Rizzo, Luca Longo
Articles
The ultimate goal of Explainable Artificial Intelligence is to build models that possess both high accuracy and degree of explainability. Understanding the inferences of such models can be seen as a process that discloses the relationships between their input and output. These relationships can be represented as a set of inference rules which are usually not explicit within a model. Scholars have proposed several methods for extracting rules from data-driven machine-learned models. However, limited work exists on their comparison. This study proposes a novel comparative approach to evaluate and compare the rulesets produced by four post-hoc rule extractors by employing …
Language-Driven Region Pointer Advancement For Controllable Image Captioning, Annika Lindh, Robert J. Ross, John D. Kelleher
Language-Driven Region Pointer Advancement For Controllable Image Captioning, Annika Lindh, Robert J. Ross, John D. Kelleher
Conference papers
Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in the generated natural language caption. This puts a stronger focus on producing more detailed descriptions, and opens the door for more end-user control over results. A vital component of the Controllable Image Captioning architecture is the mechanism that decides the timing of attending to each region through the advancement of a region pointer. In this paper, we propose a novel method for predicting the timing of region pointer advancement by treating the …
Dublin Smart City Data Integration, Analysis And Visualisation, Hammad Ul Ahad
Dublin Smart City Data Integration, Analysis And Visualisation, Hammad Ul Ahad
Doctoral
Data is an important resource for any organisation, to understand the in-depth working and identifying the unseen trends with in the data. When this data is efficiently processed and analysed it helps the authorities to take appropriate decisions based on the derived insights and knowledge, through these decisions the service quality can be improved and enhance the customer experience. A massive growth in the data generation has been observed since two decades. The significant part of this generated data is generated from the dumb and smart sensors. If this raw data is processed in an efficient manner it could uplift …
Energy-Based Neural Modelling For Large-Scale Multiple Domain Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Energy-Based Neural Modelling For Large-Scale Multiple Domain Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Conference papers
Scaling up dialogue state tracking to multiple domains is challenging due to the growth in the number of variables being tracked. Furthermore, dialog state tracking models do not yet explicitly make use of relationships between dialogue variables, such as slots across domains. We propose using energy-based structure prediction methods for large-scale dialogue state tracking task in two multiple domain dialogue datasets. Our results indicate that: (i) modelling variable dependencies yields better results; and (ii) the structured prediction output aligns with the dialogue slot-value constraint principles. This leads to promising directions to improve state-of-the-art models by incorporating variable dependencies into their …
Deep Learning In The Maintenance Industry, Paulo Cesar Ribeiro Silva
Deep Learning In The Maintenance Industry, Paulo Cesar Ribeiro Silva
Doctoral
No abstract provided.
A Hybrid Agent-Based And Equation Based Model For The Spread Of Infectious Diseases, Elizabeth Hunter, Brian Mac Namee, John D. Kelleher
A Hybrid Agent-Based And Equation Based Model For The Spread Of Infectious Diseases, Elizabeth Hunter, Brian Mac Namee, John D. Kelleher
Articles
Both agent-based models and equation-based models can be used to model the spread of an infectious disease. Equation-based models have been shown to capture the overall dynamics of a disease outbreak while agent-based models are able to capture heterogeneous characteristics of agents that drive the spread of an outbreak. However, agent-based models are computationally intensive. To capture the advantages of both the equation-based and agent-based models, we create a hybrid model where the disease component of the hybrid model switches between agent-based and equation-based. The switch is determined using the number of agents infected. We first test the model at …
Comparing Variable Importance In Prediction Of Silence Behaviours Between Random Forest And Conditional Inference Forest Models., Stephen Barrett Dr, Geraldine Gray Dr, Colm Mcguinness Dr, Michael Knoll Dr.
Comparing Variable Importance In Prediction Of Silence Behaviours Between Random Forest And Conditional Inference Forest Models., Stephen Barrett Dr, Geraldine Gray Dr, Colm Mcguinness Dr, Michael Knoll Dr.
Articles
This paper explores variable importance metrics of Conditional Inference Trees (CIT) and classical Classification And Regression Trees (CART) based Random Forests. The paper compares both algorithms variable importance rankings and highlights why CIT should be used when dealing with data with different levels of aggregation. The models analysed explored the role of cultural factors at individual and societal level when predicting Organisational Silence behaviours.
Cleanpage: Fast And Clean Document And Whiteboard Capture, Jane Courtney
Cleanpage: Fast And Clean Document And Whiteboard Capture, Jane Courtney
Articles
The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and …
F-Measure Optimisation And Label Regularisation For Energy-Based Neural Dialogue State Tracking Models, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
F-Measure Optimisation And Label Regularisation For Energy-Based Neural Dialogue State Tracking Models, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Conference papers
In recent years many multi-label classification methods have exploited label dependencies to improve performance of classification tasks in various domains, hence casting the tasks to structured prediction problems. We argue that multi-label predictions do not always satisfy domain constraint restrictions. For example when the dialogue state tracking task in task-oriented dialogue domains is solved with multi-label classification approaches, slot-value constraint rules should be enforced following real conversation scenarios.
To address these issues we propose an energy-based neural model to solve the dialogue state tracking task as a structured prediction problem. Furthermore we propose two improvements over previous methods with respect …
Misogyny Detection In Social Media On The Twitter Platform, Elena Shushkevich
Misogyny Detection In Social Media On The Twitter Platform, Elena Shushkevich
Doctoral
The thesis is devoted to the problem of misogyny detection in social media. In the work we analyse the difference between all offensive language and misogyny language in social media, and review the best existing approaches to detect offensive and misogynistic language, which are based on classical machine learning and neural networks. We also review recent shared tasks aimed to detect misogyny in social media, several of which we have participated in. We propose an approach to the detection and classification of misogyny in texts, based on the construction of an ensemble of models of classical machine learning: Logistic Regression, …
Homo Ludens Moralis: Designing And Developing A Board Game To Teach Ethics For Ict Education, Damian Gordon, Dympna O'Sullivan, Ioannis Stavrakakis, Andrea Curley
Homo Ludens Moralis: Designing And Developing A Board Game To Teach Ethics For Ict Education, Damian Gordon, Dympna O'Sullivan, Ioannis Stavrakakis, Andrea Curley
Conference papers
The ICT ethical landscape is changing at an astonishing rate, as technologies become more complex, and people choose to interact with them in new and distinct ways, the resultant interactions are more novel and less easy to categorise using traditional ethical frameworks. It is vitally important that the developers of these technologies do not live in an ethical vacuum; that they think about the uses and abuses of their creations, and take some measures to prevent others being harmed by their work.
To equip these developers to rise to this challenge and to create a positive future for the use …
Check Your Tech – Considering The Provenance Of Data Used To Build Digital Products And Services: Case Studies And An Ethical Checksheet, Dympna O'Sullivan, Damian Gordon
Check Your Tech – Considering The Provenance Of Data Used To Build Digital Products And Services: Case Studies And An Ethical Checksheet, Dympna O'Sullivan, Damian Gordon
Conference papers
Digital products and services are producing unprecedented amounts of data worldwide. These products and services have broad reach and include many users and consumers in the developing world. Once data is collected it is often used to create large and valuable datasets. A lack of data protection regulation in the developing world has led to concerns about digital colonization and a lack of control of their data on the part of citizens in the developing world. The authors of this paper are developing a new digital ethics curriculum for the instruction of computer science students. In this paper we present …
Modulation Of Medical Condition Likelihood By Patient History Similarity, Jonathan Turner, Dympna O'Sullivan, Jon Bird
Modulation Of Medical Condition Likelihood By Patient History Similarity, Jonathan Turner, Dympna O'Sullivan, Jon Bird
Articles
Introduction: We describe an analysis that modulates the simple population prevalence derived likelihood of a particular condition occurring in an individual by matching the individual with other individuals with similar clinical histories and determining the prevalence of the condition within the matched group.
Methods: We have taken clinical event codes and dates from anonymised longitudinal primary care records for 25,979 patients with 749,053 recorded clinical events. Using a nearest neighbour approach, for each patient, the likelihood of a condition occurring was adjusted from the population prevalence to the prevalence of the condition within those patients with the closest matching clinical …
An Application Of Machine Learning To Explore Relationships Between Factors Of Organisational Silence And Culture, With Specific Focus On Predicting Silence Behaviours, Stephen Barrett Dr
An Application Of Machine Learning To Explore Relationships Between Factors Of Organisational Silence And Culture, With Specific Focus On Predicting Silence Behaviours, Stephen Barrett Dr
Articles
Research indicates that there are many individual reasons why people do not speak up when confronted with situations that may concern them within their working environment. One of the areas that requires more focused research is the role culture plays in why a person may remain silent when such situations arise. The purpose of this study is to use data science techniques to explore the patterns in a data set that would lead a person to engage in organisational silence. The main research question the thesis asks is: Is Machine Learning a tool that Social Scientists can use with respect …
A Model For The Spread Of Infectious Diseases In A Region, Elizabeth Hunter, Brian Mac Namee, John D. Kelleher
A Model For The Spread Of Infectious Diseases In A Region, Elizabeth Hunter, Brian Mac Namee, John D. Kelleher
Articles
In understanding the dynamics of the spread of an infectious disease, it is important to understand how a town’s place in a network of towns within a region will impact how the disease spreads to that town and from that town. In this article, we take a model for the spread of an infectious disease in a single town and scale it up to simulate a region containing multiple towns. The model is validated by looking at how adding additional towns and commuters influences the outbreak in a single town. We then look at how the centrality of a town …
Automatic Flood Detection In Sentinei-2 Images Using Deep Convolutional Neural Networks, Pallavi Jain, Bianca Schoen-Phelan, Robert J. Ross
Automatic Flood Detection In Sentinei-2 Images Using Deep Convolutional Neural Networks, Pallavi Jain, Bianca Schoen-Phelan, Robert J. Ross
Conference papers
The early and accurate detection of floods from satellite imagery can aid rescue planning and assessment of geophysical damage. Automatic identification of water from satellite images has historically relied on hand-crafted functions, but these often do not provide the accuracy and robustness needed for accurate and early flood detection. To try to overcome these limitations we investigate a tiered methodology combining water index like features with a deep convolutional neural network based solution to flood identification against the MediaEval 2019 flood dataset. Our method builds on existing deep neural network methods, and in particular the VGG16 network. Specifically, we explored …
Incorporating Digital Ethics Throughout The Software Development Process, Michael Collins, Damian Gordon, Anna Becevel, William O'Mahony
Incorporating Digital Ethics Throughout The Software Development Process, Michael Collins, Damian Gordon, Anna Becevel, William O'Mahony
Conference papers
The media is reporting scandals associated with computer companies with increasing regularity; whether it is the misuse of user data, breach of privacy concerns, the use of biased artificial intelligence, or the problems of automated vehicles. Because of these complex issues, there is a growing need to equip computer science students with a deep appreciation of ethics, and to ensure that in the future they will develop computer systems that are ethically-based. One particularly useful strand of their education to incorporate ethics into is when teaching them about the formal approaches to developing computer systems.
There are a number of …
Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynali
Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynali
Dissertations
Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an …
Preface To The Special Issue On Advances In Argumentation In Artificial Intelligence, Pierpaolo Dondio, Luca Longo, Stefano Bistarelli
Preface To The Special Issue On Advances In Argumentation In Artificial Intelligence, Pierpaolo Dondio, Luca Longo, Stefano Bistarelli
Articles
Now at the forefront of automated reasoning, argumentation has become a key research topic within Artificial Intelligence. It involves the investigation of those activities for the production and exchange of arguments, where arguments are attempts to persuade someone of something by giving reasons for accepting a particular conclusion or claim as evident. The study of argumentation has been the focus of attention of philosophers and scholars, from Aristotle and classical rhetoric to the present day. The computational study of arguments has emerged as a field of research in AI in the last two decades, mainly fuelled by the interest from …
Beyond Reasonable Doubt: A Proposal For Undecidedness Blocking In Abstract Argumentation, Pierpaolo Dondio, Luca Longo
Beyond Reasonable Doubt: A Proposal For Undecidedness Blocking In Abstract Argumentation, Pierpaolo Dondio, Luca Longo
Articles
In Dung’s abstract semantics, the label undecided is always propagated from the attacker to the attacked argument, unless the latter is also attacked by an accepted argument. In this work we propose undecidedness blocking abstract argumentation semantics where the undecided label is confined to the strong connected component where it was generated and it is not propagated to the other parts of the argumentation graph. We show how undecidedness blocking is a fundamental reasoning pattern absent in abstract argumentation but present in similar fashion in the ambiguity blocking semantics of Defeasible logic, in the beyond reasonable doubt legal principle or …
An International Pilot Study Of K-12 Teachers’Computer Science Self-Esteem, Rebecca Vivian, Katrina Falkner, Leonard Busuttil, Keith Quille, Sue Sentance, Elizabeth Cole, Francesco Maiorana, Monica M. Mcgil, Sarah Barksdale, Christine Liebe
An International Pilot Study Of K-12 Teachers’Computer Science Self-Esteem, Rebecca Vivian, Katrina Falkner, Leonard Busuttil, Keith Quille, Sue Sentance, Elizabeth Cole, Francesco Maiorana, Monica M. Mcgil, Sarah Barksdale, Christine Liebe
Conference Papers
Computer Science (CS) is a new subject area for many K-12 teachersaround the world, requiring new disciplinary knowledge and skills.Teacher social-behavioral factors (e.g. self-esteem) have been foundto impact learning and teaching, and a key part of CS curriculumimplementation will need to ensure teachers feel confident to de-liver CS. However, studies about CS teacher self-esteem are lacking.This paper presents an analysis of publicly available data (n=219)from a pilot study using a Teacher CS Self-Esteem scale. Analy-sis revealed significant differences, including 1) females reportedsignificantly lower CS self-esteem than males, 2) primary teachersreported lower levels of CS self-esteem than secondary teachers, 3)those with …
Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev
Dissertations
Modeling non-stationary time series data is a difficult problem area in AI, due to the fact that the statistical properties of the data change as the time series progresses. This complicates the classification of non-stationary time series, which is a method used in the detection of brain diseases from EEGs. Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures achieving a high degree of success. This study implements a new, spiking neural network-based approach to time series classification for the purpose of …
Expectations Of Artificial Intelligence And The Performativity Of Ethics: Implications For Communication Governance, Aphra Kerr, Marguerite Barry, John D. Kelleher
Expectations Of Artificial Intelligence And The Performativity Of Ethics: Implications For Communication Governance, Aphra Kerr, Marguerite Barry, John D. Kelleher
Articles
This article draws on the sociology of expectations to examine the construction of expectations of ‘ethical AI’ and considers the implications of these expectations for communication governance. We first analyse a range of public documents to identify the key actors, mechanisms and issues which structure societal expectations around artificial intelligence (AI) and an emerging discourse on ethics. We then explore expectations of AI and ethics through a survey of members of the public. Finally, we discuss the implications of our findings for the role of AI in communication gover- nance. We find that, despite societal expectations that we can design …
Finding Common Ground For Citizen Empowerment In The Smart City, John D. Kelleher, Aphra Kerr
Finding Common Ground For Citizen Empowerment In The Smart City, John D. Kelleher, Aphra Kerr
Articles
Corporate smart city initiatives are just one example of the contemporary culture of surveillance. They rely on extensive information gathering systems and Big Data analysis to predict citizen behaviour and optimise city services. In this paper we argue that many smart city and social media technologies result in a paradox whereby digital inclusion for the purposes of service provision also results in marginalisation and disempowerment of citizens. Drawing upon insights garnered from a digital inclusion workshop conducted in the Galapagos islands, we propose that critically and creatively unpacking the computational techniques embedded in data services is needed as a first …
Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone
Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone
Dissertations
The CIRSY system (or Chick Instance Recognition System) is am image processing system developed as part of this research to detect images of chicks in highly-populated images that uses the leading algorithm in instance segmentation tasks, called the Mask R-CNN. It extends on the Faster R-CNN framework used in object detection tasks, and this extension adds a branch to predict the mask of an object along with the bounding box prediction. Mask R-CNN has proven to be effective ininstance segmentation and object de-tection tasks after outperforming all existing models on evaluation of the Microsoft Common Objects in Context (MS COCO) …
Importance Of Data Distribution On Hive-Based Systems For Query Performance: An Experimental Study, Hilmi Egemen Ciritoglu, John Murphy, Christina Thorpe
Importance Of Data Distribution On Hive-Based Systems For Query Performance: An Experimental Study, Hilmi Egemen Ciritoglu, John Murphy, Christina Thorpe
Articles
SQL-on-Hadoop systems have been gaining popularity in recent years. One popular example of SQL-on-Hadoop systems is Apache Hive; the pioneer of SQL-on-Hadoop systems. Hive is located on the top of big data stack as an application layer. Besides the application layer, the Hadoop Ecosystem is composed of 3 different main layers: storage, the resource manager and processing engine. The demand from industry has led to the development of new efficient components for each layer. As the ecosystem evolves over time, Hive employed different execution engines too. Understanding the strengths of components is very important in order to exploit the full …
Analysis Of Automatic Annotations Of Real Video Surveillance Images, Diana Guevara Flores, Fernando Pérez Téllez, David Pinto Avendaño
Analysis Of Automatic Annotations Of Real Video Surveillance Images, Diana Guevara Flores, Fernando Pérez Téllez, David Pinto Avendaño
Articles
The results of the analysis of the automatic annotations of real video surveillance sequences are presented. The annotations of the frames of surveillance sequences of the parking lot of a university campus are generated. The purpose of the analysis is to evaluate the quality of the descriptions and analyze the correspondence between the semantic content of the images and the corresponding annotation. To perform the tests, a fixed camera was placed in the campus parking lot and video sequences of about 20 minutes were obtained, later each frame was annotated individually and a text repository with all the annotations was …