Physical Sciences and Mathematics Commons^™

Development Of A Photopolymer Holographic Lens For Collimation Of Light From A Green Light-Emitting Diode, Sanjay Keshri, Kevin Murphy, Vincent Toal, Izabela Naydenova, Suzanne Martin

Articles

A holographic optical element (HOE) was developed to collimate a monochromatic light-emitting diode (LED). The fabrication was achieved by the interference of collimated and diverging beams from a 532 nm laser to produce the required photonic structure in a self-developing photopolymer material. The experimental values of diffraction efficiency and spatial period across the HOE were compared with their expected theoretical values. Good agreement was found for the spatial period; however, the diffraction efficiency varied significantly across the lens. In this paper, two approaches have been taken to address this variation: (1) modification of recording geometry and (2) optimization of recording …

Self-Processing Photopolymer Materials For Versatile Design And Fabrication Of Holographic Sensors And Interactive Holograms, Dervil Cody, Sabad-E Gul, Tatsiana Mikulchyk, Muhammad Irfan, Anastasia Kharchenko, Kamila Goldyn, Suzanne Martin, Svetlana Mintova, John Cassidy, Izabela Naydenova

Articles

The aim of this paper is to discuss the benefits as well as the limitations of utilizing photopolymer materials in the design of holograms that are responsive to changes in their environment, such as changes in the concentration of a specific substance, temperature, and pressure. Three different case studies are presented, including both surface and volume phase holograms, in order to demonstrate the flexibility in the approach of utilizing holographic photopolymers for the design of sensors and interactive optical devices. First, a functionalized surface relief hologram is demonstrated to operate as an optical sensor for the detection of metal ions …

Absorption Coefficient Dependent Non-Linear Properties Of Thin Film Luminescent Solar Concentrators, S. Chandra, M. Rafiee, John Doran, S. J. Mccormack

Articles

This research article studied absorption coefficient dependent losses and efficiency of thin film luminescent solar concentrator (LSC). The optimum absorption coefficient was determined by fabricating red dye doped thin film LSCs with varying absorption coefficients of 24–202 cm-1. The optical, electrical, and ray-trace model predicted results were compared to establish the optimum absorption coefficient for fabricated thin film LSCs. The optical and electrical efficiencies of thin film LSCs are a function of absorption coefficient; follow linearly at lower values, gradually becoming non- linear at higher values of absorption coefficient which restricts the overall efficiency. The multi layered ray–trace model predicted …

Development Of A Hierarchical Model For Predicting Microbiological Contamination Of Private Groundwater Supplies In A Geologically Heterogeneous Region, Jean O'Dwyer, Paul Hynds, Kenneth Byrne, Michael Ryan, Catherine Adley

Articles

Private groundwater sources in the Republic of Ireland provide drinking water to an estimated 750,000 people or 16% of the national population. Consumers of untreated groundwater are at increased risk of infection from pathogenic microorganisms. However, given the volume of private wells in operation, remediation or even quantification of public risk is both costly and time consuming. In this study, a hierarchical logistic regression model was developed to ‘predict’ contamination with E. coli based on the results of groundwater quality analyses of private wells (n = 132) during the period of September 2011 to November 2012. Assessment of potential microbial …

Information Hiding Using Convolutional Encoding, Jonathan Blackledge, Paul Tobin, J. Myeza, C. M. Adolfo

Conference papers

We consider two functions f1(r) and f2(r), for r 2 Rn and the problem of ‘Diffusing’ these functions together, followed by the application of an encryption process we call ‘Stochastic Diffusion’ and then hiding the output of this process in to one or other of the same functions. The coupling of these two processes (i.e., data diffusion and stochastic diffusion) is considered using a form of conditioning that generates a wellposed and data consistent inverse solution for the purpose of decrypting the output. After presenting the basic encryption method and (encrypted) information hiding model, coupled with a mathematical analysis (within …

Interoperable Ocean Observing Using Archetypes: A Use-Case Based Evaluation, Paul Stacey, Damon Berry

Conference papers

This paper presents a use-case based evaluation of the impact of two-level modeling on the automatic federation of ocean observational data. The goal of the work is to increase the interoperability and data quality of aggregated ocean observations to support convenient discovery and consumption by applications. An assessment of the interoperability of served data flows from publicly available ocean observing spatial data infrastructures was performed. Barriers to consumption of existing standards-compliant ocean-observing data streams were examined, including the impact of adherence to agreed data standards. Historical data flows were mapped to a set of archetypes and a backward integration experiment …

Towards A Conceptual Framework For The Development Of Immersive Experiences To Negotiate Meaning And Identify In Irish Language Learning, Naoise Collins, Brian Vaughan, Keith Gardiner, Charlie Cullen

Conference papers

The onset of virtual reality systems allows for new immersive content which provides users with a sense of presence in their virtual environment. This paper provides the conceptual framework for a larger study examining how designed virtual reality experiences can be utilised to transform Irish language meaning making and a user's personal Irish language identity.

Menstrual Cycle And The Temporal Discrimination Threshold, Eavan M. Mc Govern, Emer O'Connor, Ines Beiser, Laura Williams, John Butler, Brendan Quinlivan, Shruti Narasimham, Rebecca Beck, Richard B. Reilly, Sean O'Riordan, Michael Hutchinson

Articles

The temporal discrimination threshold (TDT) is a proposed pre-clinical biomarker (endophenotype) for adult onset isolated focal dystonia (AOIFD). Age- and sex-related effects on temporal discrimination demonstrate that women, before the age of 40 years, have faster temporal discrimination than men but their TDTs worsen with age at almost three times the rate of men. Thus after 40 years the TDT in women is progressively worse than in men. AOIFD is an increasingly female-predominant disorder after the age of 40; it is not clear whether this age-related sexually-dimorphic difference observed for both the TDT and sex ratio at disease onset in …

Cognitive Load Reduces The Effects Of Optic Flow On Gait And 2 Electrocortical Dynamics During Treadmill Walking 3, Brenda Malcolm, John J. Foxe, John Butler, Sophie Molholm, Pierfilippo De Sanctis

Articles

While navigating complex environments the brain must continuously adapt to both external demands such as fluctuating sensory inputs, as well as internal demands, such as engagement in a cognitively demanding task. Previous studies have demonstrated changes in behavior and gait with increased sensory and cognitive load, but the underlying cortical mechanisms remain largely unknown. Here, in a Mobile Brain/Body Imaging (MoBI) approach sixteen young adults walked on a treadmill with high-density EEG while 3D motion capture tracked kinematics of the head and feet. Visual load was manipulated with the presentation of optic flow with and without continuous mediolateral perturbations. The …

An Investigation Of The Impact Of Language Runtime On The Performance And Cost Of Serverless Functions, David Jackson, Gary Clynch

Conference Papers

Serverless, otherwise known as “Function-as-a- Service” (FaaS), is a compelling evolution of cloud computing that is highly scalable and event-driven. Serverless applications are composed of multiple independent functions, each of which can be implemented in a range of programming languages. This paper seeks to understand the impact of the choice of language runtime on the performance and subsequent cost of serverless function execution. It presents the design and implementation of a new serverless performance testing framework created to analyse performance and cost metrics for both AWS Lambda and Azure Functions. For optimum performance and cost management of serverless applications, Python …

Programming: Predicting Student Success Early In Cs1. A Re-Validation And Replication Study, Keith Quille, Susan Bergin

Articles

This paper describes a large, multi-institutional revalidation study conducted in the academic year 2015-16. Six hundred and ninetytwo students participated in this study, from 11 institutions (ten institutions in Ireland and one in Denmark). The primary goal was to validate and further develop an existing computational prediction model called Predict Student Success (PreSS). In doing so, this study addressed a call from the 2015 ITiCSE working group (the second "Grand Challenge"), to "systematically analyse and verify previous studies using data from multiple contexts to tease out tacit factors that contribute to previously observed outcomes". PreSS was developed and validated in …

Rhythm Inference From Audio Recordings Of Irish Traditional Music, Pierre Beauguitte, Bryan Duggan, John D. Kelleher

Conference papers

A new method is proposed to infer rhythmic information from audio recordings of Irish traditional tunes. The method relies on he repetitive nature of this musical genre. Low-level spectral features and autocorrelation are used to obtain a low-dimensional representation, on which logistic regression models are trained. Two experiments are conducted to predict rhythmic information at different levels of precision. The method is tested on a collec- ion of session recordings, and high accuracy scores are reported.

A new method is proposed to infer rhythmic information from audio recordings of Irish traditional tunes. The method relies on he repetitive nature of …

An Investigation Of The Impact Of A Social Constructivist Teaching Approach, Based On Trigger Questions, Through Measures Of Mental Workload And Efficiency, Federico Gobbo, Luca Longo, Declan O'Sullivan, Giuliano Orru

Conference papers

Social constructivism is grounded on the construction of information with a focus on collaborative learning through social interactions. However, it tends to ignore the human mental architecture, pillar of cognitivism. A characteristic of cognitivism is that instructional designs built upon it are generally explicit, contrarily to constructivism. This position paper proposes a novel learning task that is aimed at combining both the approaches through the use of trigger questions in a collaborative activity executed after a traditional delivery of instructions. To evaluate this new task, a metric of efficiency based upon a measure of mental workload and a measure of …

Validation Of Tagging Suggestion Models For A Hotel Ticketing Corpus, Bojan Bozic, Andre Rios, Sarah Jane Delany

Conference papers

This paper investigates methods for the prediction of tags on a textual corpus that describes hotel staff inputs in a ticketing system. The aim is to improve the tagging process and find the most suitable method for suggesting tags for a new text entry. The paper consists of two parts: (i) exploration of existing sample data, which includes statistical analysis and visualisation of the data to provide an overview, and (ii) evaluation of tag prediction approaches. We have included different approaches from different research fields in order to cover a broad spectrum of possible solutions. As a result, we have …

Non-Linear Machine Learning With Active Sampling For Mox Drift Compensation, Tamara Matthews, Muhammad Iqbal, Horacio Gonzalez-Velez

Conference papers

Abstract—Metal oxide (MOX) gas detectors based on SnO2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor longterm response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, …

Sensory Seduction And Narrative Pull, Nina Lyons, Matt Smith, Hugh Mccabe

Conference papers

User experience design is a process that has been defined, developed and refined over the last few decades. It is a process of shaping a user's movements through a website or mobile application. It is user-focussed, prioritising utility, ease-of-use and efficiency. It is widely used and has helped advance the way in which users interact with websites and mobile applications, making it far less frustrating. User experience design is a key element in how the internet and mobile technology have become ubiquitous in our daily lives. Given this success, it would seem that continuing to use this process for new …

Predicting Happiness - Comparison Of Supervised Machine Learning Techniques Performance On A Multiclass Classification Problem, Dorota Nieciecka

Dissertations

In the modern world, especially in contemporary economies and politics, a population's subjective well-being is a frequent subject of the public debate. As comparisons of happiness levels in different countries are published, different circumstances and their effect on the value of the subjective well-being reported by people are also analysed. However, a significant amount of the research related to subjective well-being and its determinants is still based upon survey answers and employing conventional statistical methods providing details regarding correlations and causality between different factors and subjective well-being. Application of Supervised Machine Learning techniques for prediction of subjective well-being may provide …

A Demographic Analysis To Determine User Vulnerability Among Several Categories Of Phishing Attacks., Robert Griffin

Dissertations

Phishing attacks have been on a meteoric rise in the last number of years, with 2016 seeing a 65% increase. The attacks range from targeting individuals with personalised messages to spam attacks from bot accounts. With the chances of being targeted by a phishing attack increasing, it is important to identify who is most at risk in order to help alleviate this threat. The aim of this study is to examine members from several demographics and their vulnerability to three types of phishing using data collected from a survey (n = 198). The survey tested the participant’s ability to recognise …

An Investigation Into Factors Which Explain The Scores And Voting Patterns Of The Eurovision Song Contest., Oisín Leonard

Dissertations

The Eurovision Song Contest (ESC) is an annual international television song competition. Participating countries send a group or individual artist to perform an original song at the competition. The winner is decided by all participating countries using a voting system that incorporates both a public televote and an expert jury vote. Countries are excluded from voting for their entry and the country with the highest score wins. A high scoring performance and the voting patterns of the ESC can be explained by a complex set of factors. These factors can be divided into three groups; performance factors, competition factors and …

Image Classification Using Bag-Of-Visual-Words Model, Kaiqiang Huang

Dissertations

Recently, with the explosive growth of digital technologies, there has been a rapid proliferation of the size of image collection. The technique of supervised image clas sification has been widely applied in many domains in order to organize, search, and retrieve images. However, the traditional feature extraction approaches yield the poor classification accuracy. Therefore, the Bag-of-visual-words model, inspired by Bag-of Words model in document classification, was used to present images with the local descriptors for image classification, and also it performs well in some fields. This research provides the empirical evidence to prove that the BoVW model outperforms the traditional …

A Javascript Framework Comparison Based On Benchmarking Software Metrics And Environment Configuration, Jefferson Ferreira

Dissertations

JavaScript is a client-side programming language that can be used in multi-platform applications. It controls HTML and CSS to manipulate page behaviours and is widely used in most websites over the internet. JavaScript frameworks are structures made to help web developers build web applications faster by offering features that enhance the user interaction with the web page. An increasing number of JavaScript frameworks have been released in recent years in the market to help front-end developers build applications in a shorter space of time. Decision makers in software companies have been struggling to determine which frameworks are best suited for …

That Seems Made Up: Deep Learning Classifiers For Fiction & Non Fiction Book Reviews, Clement Manger

Dissertations

The thesis aims to take the first step towards automated extraction of the information found in book reviews, by using machine learning tools to assign a label of fiction or non fiction to the text. The thesis makes use of neural networks and performs experiments around architecture, hyper-parameters and text processing from which an optimized model is produced. The thesis enjoys certain successes; it was possible to match the state of the art achieved by (Kim, 2014) and computation was sped up considerably from the default to the optimized model by 13.8 seconds per 50 steps. Further it is confirmed …

A Systems Approach To Research And Innovation For Food System Transformation, M. Gill, A.C.L. Den Boer, K.P. Kok, Jean Cahill, C. Callenius, P. Caron, Z. Damianova, M.A. Gurinovic, L. Lahteenmaki, T. Lang, A. Lappiere, C. Mango, J. Ryder, R. Sonnino, H. Westhoek, B.J. Regeer, J.E.W. Broerse

Research and Innovation - Other

This policy brief of the European Union (EU) Think Tank – part of the FIT4FOOD2030 Coordination and Support Action (CSA) of the FOOD 2030 initiative – is a response and contribution to growing pleas for a ‘systems approach’ to transform food systems for Food and Nutrition Security (FNS) for present and future generations. This policy brief specifically focusses on the necessity of the adoption of a systems approach to Research and Innovation (R&I) in order to foster the transformation of food systems.

Presenting A Hybrid Processing Mining Framework For Automated Simulation Model Generation, Susan Mckeever, Mohammad Messabah

Conference papers

Recent advances in information technology systems have enabled organizations to store tremendous amounts of business process data. Process mining offers a range of algorithms and methods to analyze and extract metadata for these processes. This paper presents a novel approach to the hybridization of process mining techniques with business process modelling and simulation methods. We present a generic automated end-to-end simulation framework that produces unbiased simulation models using system event logs. A conceptual model and various meta-data are derived from the logs and used to generate the simulation model. We demonstrate the efficacy of our framework using a business process …

Pathways To The Cosmos:The Alignment Of Megalithic Tombs In Ireland And Atlantic Europe, Frank Prendergast, Gabriel Cooney

Articles

Gabriel Cooney and Frank Prendergast present the context of and outline the approach to this one-day conference organised by Archaeology Ireland on behalf of the National Monuments Service at the Department of Culture, Heritage and the Gaeltacht and the Office of Public Works to mark the European Year of Cultural Heritage 2018. The event will take place at Dublin Castle on 15 September 2018.

Survivability Strategies For Emerging Wireless Networks With Data Mining Techniques: A Case Study With Netlogo And Rapidminer, Ivan Garcia-Magarino, Geraldine Gray, Raquel Lacuesta, Jaime Lloret

Articles

Emerging wireless networks have brought Internet and communications to more users and areas. Some of the most relevant emerging wireless technologies are Worldwide Interoperability for Microwave Access, Long-Term Evolution Advanced, and ad hoc and mesh networks. An open challenge is to ensure the reliability and robustness of these networks when individual components fail. The survivability and performance of these networks can be especially relevant when emergencies arise in rural areas, for example supporting communications during a medical emergency. This can be done by anticipating failures and finding alternative solutions. This paper proposes using big data analytics techniques, such as decision …

Generating Diverse And Meaningful Captions: Unsupervised Specificity Optimization For Image Captioning, Annika Lindh, Robert J. Ross, Abhijit Mahalunkar, Giancarlo Salton, John D. Kelleher

Conference papers

Image Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsupervised training approach that incorporates a learning signal from an Image Retrieval model. We summarize previous results and improve the state-of-the-art on caption diversity and novelty.

We make our …

The Terror Network Industrial Complex: A Measurement And Analysis Of Terrorist Networks And War Stocks, James Usher, Pierpaolo Dondio

Conference papers

This paper presents a measurement study and analysis of the structure of multiple Islamic terrorist networks to determine if similar characteristics exist between those networks. We examine data gathered from four terrorist groups: Al-Qaeda, ISIS, Lashkar-e-Taiba (LeT) and Jemaah Islamiyah (JI) consisting of six terror networks. Our study contains 471 terrorists’ nodes and 2078 links. Each terror network is compared in terms efficiency, communication and composition of network metrics. The paper examines the effects these terrorist attacks had on US aerospace and defence stocks (herein War stocks). We found that the Islamic terror groups increase recruitment during the planned attacks, …

A Proposal To Embed The In Dubio Pro Reo Principle Into Abstract Argumentation Semantics Based On Topological Ordering And Undecidedness Propagation, Pierpaolo Dondio, Luca Longo

Conference papers

Abstract. In this paper we discuss how the in dubio pro reo principle and the corresponding standard of proof beyond reasonable doubt can be modelled in abstract argumentation. The in dubio pro reo principle protects arguments against attacks from doubtful arguments. We identify doubtful arguments with a subset of undecided arguments, called active undecided arguments, consisting of cyclic arguments responsible for generating the undecided situation. We obtain the standard of proof beyond reasonable doubt by imposing that attacks from doubtful undecided arguments are not enough to change the acceptability status of an attacked argument (the reo). The resulting semantics, called …

A Comparison Of Classical Versus Deep Learning Techniques For Abusive Content Detection On Social Media Sites, Hao Che, Susan Mckeever, Sarah Jane Delany

Conference papers

The automated detection of abusive content on social media websites faces a variety of challenges including imbalanced training sets, the identification of an appropriate feature representation and the selection of optimal classifiers. Classifiers such as support vector machines (SVM), combined with bag of words or ngram feature representation, have traditionally dominated in text classification for decades. With the recent emergence of deep learning and word embeddings, an increasing number of researchers have started to focus on deep neural networks. In this paper, our aim is to explore cutting-edge techniques in automated abusive content detection. We use two deep learning approaches: …