Physical Sciences and Mathematics Commons^™

A Framework For Validating And Testing Agent-Based Models: A Case Study From Infectious Diseases Modelling., Elizabeth Hunter, John D. Kelleher

Conference papers

In this paper we present a framework for validating and testing an agent-based model that includes determining the appropriate number of runs to account for variation in model runs, validating the model and showing that the model can be used to learn about the system. To demonstrate the framework we use a case study of an agent-based model for the spread of infectious diseases.

Mutual Information Decay Curves And Hyper-Parameter Grid Search Design For Recurrent Neural Architectures, Abhijit Mahalunkar, John Kelleher

Conference papers

We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures. The basis for this approach is the use of mutual information to analyze long distance dependencies (LDDs) within a dataset. We also report a set of experiments that demonstrate how using this approach, we obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.

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 …

Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar

Dissertations

Machine learning approaches are applied across several domains to either simplify or automate tasks which directly result in saved time or cost. Text document labelling is one such task that requires immense human knowledge about the domain and efforts to review, understand and label the documents. The company Stare Decisis summarises legal judgements and labels them as they are made available on Irish public legal source www.courts.ie. This research presents a recommendation-based approach to reduce the time for solicitors at Stare Decisis by reducing many numbers of available labels to pick from to a concentrated few that potentially contains the …

Research And Innovation As A Catalyst For Food System Transformation, A.C.L. Den Boer, K.P.W. Kok, M. Gill, J. Breda, Jean Cahill, C. Callenius, P. Caron, Z. Damianova, M. Gurinovic, L. Lahteenmaki, T. Lang, R. Sonnino, G. Verburg, H. Westhoek, T. Cesuroglu, B.J. Regeer, J.E.W. Broerse

Research and Innovation - Other

Background Food systems are associated with severe and persistent problems worldwide. Governance approaches aiming to foster sustainable transformation of food systems face several challenges due to the complex nature of food systems.

Scope and approach In this commentary we argue that addressing these governance challenges requires the development and adoption of novel research and innovation (R&I) approaches that will provide evidence to inform food system transformation and will serve as catalysts for change. We first elaborate on the complexity of food systems (transformation) and stress the need to move beyond traditional linear R&I approaches to be able to respond to …

Β-Methylumbelliferone Surface Modification And Permeability Investigations At Pentel™ Graphite Electrodes, Susan Warren, Brian Seddon, Ruth Pilkington, Alison Crossely, Philip Holdway, Eithne Dempsey

Articles

Electrochemical and micro-imaging analysis of a commercial graphite-composite material is presented following electro-oxidation with β-methylumbelliferone. Charge-transfer surface modification was observed for the graphite electrode, presumed to have arisen from adsorbed interfacial umbelliferone moieties. The molecular permeability of the new surface towards a range of similar, yet size-variable (23 Å3–136 Å3) molecular redox probes is discussed. Red-shift fluorescence in confocal microscopy offers further support for the presence of a surface-bound umbelliferone layer. An SEM-platinum profiling technique was used as an imaging tool to map the umbelliferone surface and size-distribution of electroactive sites.

Use Of Confocal Raman Microscopy To Characterise Ethyl Cyanoacrylate Adhesive Depth Curing, Kevin Raheem, John Cassidy, Tony Betts, Bernard Ryan

Articles

In situ spatial temporal measurement of monomer conversion during adhesive bondline curing remains a challenging area. The aim of this work was to demonstrate the effectiveness of using confocal Raman microscopy in a specially configured experimental set-up, as a versatile tool for measuring monomer concentration changes as a function of both time and adhesive bond depth during ethyl cyanoacrylate polymerisation. This also allowed monitoring of the extent of polymerisation at the adhesive substrate interface independently of the bulk bondline polymerisation region. Key kinetic parameters such as inhibition time tlag, rate of reaction Rmax and extent of reaction [at]max were obtained …

Smart Green Communication Protocols Based On Several-Fold Messages Extracted From Common Sequential Patterns In Uavs, Iván García-Magariño, Geraldine Gray, Raquel Lacuesta, Jaime Lloret

Articles

Green communications can be crucial for saving energy in UAVs and enhancing their autonomy. The current work proposes to extract common sequential patterns of communications to gather each common pattern into a single several- fold message with a high-level compression. Since the messages of a pattern are elapsed from each other in time, the current approach performs a machine learning approach for estimating the elapsed times using off-line training. The learned predictive model is applied by each UAV during flight when receiving a several-fold compressed message. We have explored neural networks, linear regression and correlation analyses among others. The current …

Multimodal Fusion Strategies For Outcome Prediction In Stroke, Esra Zihni, John D. Kelleher, Vince I. Madai, Ahmed Khalil, Ivana Galinovic, Jochen Fiebach, Michelle Livne, Dietmar Frey

Conference papers

Data driven methods are increasingly being adopted in the medical domain for clinical predictive modeling. Prediction of stroke outcome using machine learning could provide a decision support system for physicians to assist them in patient-oriented diagnosis and treatment. While patient-specific clinical parameters play an important role in outcome prediction, a multimodal fusion approach that integrates neuroimaging with clinical data has the potential to improve accuracy. This paper addresses two research questions: (a) does multimodal fusion aid in the prediction of stroke outcome, and (b) what fusion strategy is more suitable for the task at hand. The baselines for our experimental …

Language Model Co-Occurrence Linking For Interleaved Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from …

Modelling Interleaved Activities Using Language Models, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

We propose a new approach to activity discovery, based on the neural language modelling of streaming sensor events. Our approach proceeds in multiple stages: we build binary links between activities using probability distributions generated by a neural language model trained on the dataset, and combine the binary links to produce complex activities. We then use the activities as sensor events, allowing us to build complex hierarchies of activities. We put an emphasis on dealing with interleaving, which represents a major challenge for many existing activity discovery systems. The system is tested on a realistic dataset, demonstrating it as a promising …

Novice Learner Experiences In Software Development: A Study Of Freshman Undergraduates, Catherine Higgins, Ciaran O'Leary, Claire Mcavinia, Barry J. Ryan

Conference papers

This paper presents a study that is part of a larger research project aimed at addressing the gap in the provision of educational software development processes for freshman, novice undergraduate learners, to improve proficiency levels. With the aim of understanding how such learners problem solve in software development in the absence of a formal process, this case study examines the experiences and depth of learning acquired by a sample set of novice undergraduates. A novel adaption of the Kirkpatrick framework known as AKM-SOLO is used to frame the evaluation. The study finds that without the scaffolding of an appropriate structured …

Empowering Qualitative Research Methods In Education With Artificial Intelligence, Luca Longo

Conference papers

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches …

Explainable Artificial Intelligence: Concepts, Applications, Research Challenges And Visions, Luca Longo, Randy Goebel, Freddy Lecue, Peter Kieseberg, Andreas Holzinger

Conference papers

The development of theory, frameworks and tools for Explainable AI (XAI) is a very active area of research these days, and articulating any kind of coherence on a vision and challenges is itself a challenge. At least two sometimes complementary and colliding threads have emerged. The first focuses on the development of pragmatic tools for increasing the transparency of automatically learned prediction models, as for instance by deep or reinforcement learning. The second is aimed at anticipating the negative impact of opaque models with the desire to regulate or control impactful consequences of incorrect predictions, especially in sensitive areas like …

Design And Evaluation Of An Adventure Videogame Based In The History Of Mathematics, Mariana Rocha, Pierpaolo Dondio

Conference papers

The present paper describes the design and evaluation of an adventure videogame developed to cover the mathematics primary school curriculum. The narrative of the game is based in the history of mathematics and, to win, the player needs to travel through time, starting from the ancient Egypt and finishing at the modern world. To achieve that, the player interacts with real-life characters, such as Pythagoras of Samos, learning about their contributions to the field and using this knowledge to solve puzzles. The aim of the research presented in this paper is to understand the effects of the game on students’ …

Smpl-Based 3d Pedestrian Pose Prediction, Anil Kunchala, Bianca Schoen-Phelan, Mélanie Bouroche, Lorraine D'Arcy

Conference papers

Modeling human motion is a long-standing problem in computer vision. The rapid development of deep learning technologies for computer vision problems resulted in increased attention in the area of pose prediction due to its vital role in a multitude of applications, for example, behavior analysis, autonomous vehicles, and visual surveillance. In 3D pedestrian pose prediction, joint-rotation-based pose representation is extensively used due to the unconstrained degree of freedom for each joint and its ability to regress the 3D statistical wireframe. However, all the existing joint-rotation-based pose prediction approaches ignore the centrality of the distinct pose parameter components and are consequently …

Brexit: Psychometric Profiling The Political Salubrious Through Machine Learning: Predicting Personality Traits Of Boris Johnson Through Twitter Political Text, James Usher, Pierpaolo Dondio

Conference papers

Whilst the CIA have been using psychometric profiling for decades, Cambridge Analytica showed that people's psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook or Twitter accounts. To exploit this form of psychological assessment from digital footprints, we propose machine learning methods for assessing political personality from Twitter. We have extracted the tweet content of Prime Minster Boris Johnson’s Twitter account and built three predictive personality models based on his Twitter political content. We use a Multi-Layer Perceptron Neural network, a Naive Bayes multinomial model and a Support Machine Vector model to predict the OCEAN …

A Catalogue Of Locus Algorithm Pointings For Optimal Differential Photometry For 23,779 Quasars, Oisın Creaner, Kevin Nolan, David Grennan, Niall Smith, Eugene Hickey

Articles

This paper presents a catalogue of optimised pointings for differential photometry of23,779 quasars extracted from the Sloan Digital Sky Survey (SDSS) Catalogue and ascore for each indicating the quality of the Field of View (FoV) associated with thatpointing. Observation of millimagnitude variability on a timescale of minutes typicallyrequires differential observations with reference to an ensemble of reference stars. Foroptimal performance, these reference stars should have similar colour and magnitudeto the target quasar. In addition, the greatest quantity and quality of suitable refer-ence stars may be found by using a telescope pointing which offsets the target objectfrom the centre of the …

Comparing Tagging Suggestion Models On Discrete Corpora, Bojan Bozic, Andre Rios, Sarah Jane Delany

Articles

This paper aims to investigate the methods for the prediction of tags on a textual corpus that describes diverse data sets based on short messages; as an example, the authors demonstrate the usage of methods based on hotel staff inputs in a ticketing system as well as the publicly available StackOverflow corpus. The aim is to improve the tagging process and find the most suitable method for suggesting tags for a new text entry.

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

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 …

An Univariable Approach For Forecasting Workload In The Maintenance Industry, Paulo Silva, Fernando Pérez Téllez, John Cardiff

Articles

The forecasting of the workload in the maintenance industry is of great value to improve human resources allocation and reduce overwork. In this paper, we discuss the problem and the challenges it pertains. We analyze data from a company operating in the industry and present the results of several forecasting models.

A Comparison Of High-End Methods For Topographic Modelling Of A Coastal Dune Complex, Chen Suo, Eugene Mcgovern, Alan Gilmer, Conor Cahalane

Articles

In recent years, increasing tourism and development in the coastal dune area of the South East of Ireland have resulted in greater pressure on the environment, resulting in issues including soil erosion, flooding and habitat loss. Topographic mapping across a dune field is important for the development of targeted land management actions that maintain biodiversity and ecological functions. Developments in surveying technology, including LiDAR, terrestrial laser scanners (TLS) and aerial surveying from Remotely Piloted Aircraft Systems (RPAS), have enabled high-resolution and high-accuracy spatial data to be gathered quickly and relatively easily for 3D topographic modelling of a coastal dune complex. …

Using Machine Learning Classification Methods To Detect The Presence Of Heart Disease, Nestor Pereira

Dissertations

Cardiovascular disease (CVD) is the most common cause of death in Ireland, and probably, worldwide. According to the Health Service Executive (HSE) cardiovascular disease accounting for 36% of all deaths, and one important fact, 22% of premature deaths (under age 65) are from CVD.

Using data from the Heart Disease UCI Data Set (UCI Machine Learning), we use machine learning techniques to detect the presence or absence of heart disease in the patient according to 14 features provide for this dataset. The different results are compared based on accuracy performance, confusion matrix and area under the Receiver Operating Characteristics (ROC) …

Factor Analysis Of Mixed Data (Famd) And Multiple Linear Regression In R, Nestor Pereira

Dissertations

In the previous projects, it has been worked to statistically analysis of the factors to impact the score of the subjects of Mathematics and Portuguese for several groups of the student from secondary school from Portugal.

In this project will be interested in finding a model, hypothetically multiple linear regression, to predict the final score, dependent variable G3, of the student according to some features divide into two groups. One group, analyses the features or predictors which impact in the final score more related to the performance of the students, means variables like study time or past failures. The second …

How Do Spinal Surgeons Perceive The Impact Of Factors Used In Post-Surgical Complication Risk Scores?, Enea Parimbelli, Szymon Wilk, Dympna O'Sullivan, Stephen Kingwell, Wojtek Michalowski, Martin Michalowski

Conference papers

When deciding about surgical treatment options, an important aspect of the decision-making process is the potential risk of complications. A risk assessment performed by a spinal surgeon is based on their knowledge of the best available evidence and on their own clinical experience. The objective of this work is to demonstrate the differences in the way spine surgeons perceive the importance of attributes used to calculate risk of post-operative and quantify the differences by building individual formal models of risk perceptions. We employ a preference-learning method - ROR-UTADIS - to build surgeon-specific additive value functions for risk of complications. Comparing …

Update Frequency And Background Corpus Selection In Dynamic Tf-Idf Models For First Story Detection, Fei Wang, Robert J. Ross, John D. Kelleher

Conference papers

First Story Detection (FSD) requires a system to detect the very first story that mentions an event from a stream of stories. Nearest neighbour-based models, using the traditional term vector document representations like TF-IDF, currently achieve the state of the art in FSD. Because of its online nature, a dynamic term vector model that is incrementally updated during the detection process is usually adopted for FSD instead of a static model. However, very little research has investigated the selection of hyper-parameters and the background corpora for a dynamic model. In this paper, we analyse how a dynamic term vector model …

Sure 2019 Undergraduate Science Conference Booklet, Sure Network

Group Reports

The Second Annual Science Undergraduate Research Experience (SURE) Conferences (SURE 2019) took place on 27th September 2019 in Institute of Technology, Carlow, Technological University Dublin and Institute of Technology, Sligo. The simultaneous conferences had a total of 23 oral presentations and 67 poster presentations, and were attended by over 450 students, academic staff, professional body and industry representatives.

The aims of the conference were to:

• Provide current students with an opportunity to gain an understanding of the work which has been undertaken by recent graduates, and the career opportunities that exist for graduates in Scientific disciplines.
• Provide recent graduates with …

Editorial Issue 1, Barry Ryan

SURE Journal: Science Undergraduate Research Experience Journal

Editorial Issue 1

Genetic Variation Of The Dgat1 Gene In Dual-Purpose Dairy Cows And Its Influence On Economically Important Breeding Traits, Mark E. Armitage, Emma Swan, Denise O'Meara

SURE Journal: Science Undergraduate Research Experience Journal

Background
Over the last 10 years, the discovery of individual, or candidate, genes that influence economically important traits has revolutionised how the dairy industry selects cows for breeding programmes. One such gene, the diacylglycerol O-acyltransferase 1 (DGAT1) gene is significant in dairy production as it is associated with the synthesis of fat in milk, and polymorphisms within the gene can be used for selection purposes. In this study, the variants (genotypes) of the K232A polymorphism of the DGAT1 gene, within a dual-purpose dairy herd, bred for both dairy and beef production, were determined, and compared to the Economic …