Digital Commons Network^™

Cooking A Corporation Tax Controversy: Apple, Ireland And The Eu., Ciara Graham, Brendan O'Rourke

Articles

Given the centrality of corporations in distribution of income and wealth studies, discursive constructions of corporate taxation are essential to understanding the production of inequality. The focus of this study is an interview with Apple’s Chief Executive Tim Cook on the Irish state broadcaster, Raidió Teilifís Éireann’s (RTÉ) flagship news programme, Morning Ireland, following the ruling by the European Commission (EC) on the corporation tax arrangements between Apple Inc. and Ireland. Drawing on a Critical Discourse Analysis (CDA) approach, a frame analysis is provided. The significance and extent of the EC’s ruling has potential implications for corporation taxation policy, within …

Visions Of The Future In Budgetary Discourse, Ewan Macdonald, Brendan O'Rourke, John Hogan

Conference papers

Whilst there is ample precedent to argue against the common-sense notion that the ideological leanings of political parties are congruent with their implementation of fiscal policy (Boix, 2000; Garrett & Lange, 1991; Hibbs, 1977; Liargovas & Manolas, 2007), there is a relative dearth of research on the role of discourse in shaping fiscal policy with one notable exception by Maatsch (2014). With this in mind, we approach the issue of examining fiscal policy through a fixed, contested and subverted within particular texts” (Howarth, 2005, p. 341). This paper examines how the future is constructed in Irish budget speeches delivered between …

Entrepreneurship Book Of Vietnamese Cases, Thomas M. Cooney, Vũ Thế Dũng, Trần Hoàng Hải, Vũ Văn Thiệu, Lê Hà Như Thảo, Huỳnh Thị Kim Hà, Tạ Hùng Anh, Đinh Nguyễn Khánh Phương, Huỳnh Minh Nhựt

Books/Book Chapters

This publication ‘Entrepreneurship Book of Vietnamese Cases’ is part of the ‘Supporting Entrepreneurial Development in ICT in Vietnamese HEIs – ICTentr’ project, an initiative which aims to support the national goals of Vietnam to modernise higher education systems and improve skills in ICT through capacity development. The overall objective of this project is to provide ICT students with an entrepreneurial mindset, soft skills and a professional attitude when graduating from the university. The activities are targeted both to students and teachers involved in enhancing innovation. The project is coordinated by the University of Turku (UTU), Finland. The consortium is also …

A Guide For Vet Teachers With A Focus On Aquaculture: Using Student Rapid Response Systems (Srrs) For Formative Assessment And For Recognition Of Prior Learning (Rpl), Anne Murphy, Aidan Kenny, Martyn Haines, Christine Dudgeon, Dag Willmann

Reports

Optimal was a European Commission funded Erasmus+ Key Action 2 Strategic Partnership Project KA2-SP, titled – ‘Optimised Training: Innovative Methods and Tools for Acceptance of Prior Learning in Qualifications and Workplace Training’. It started in 2016 and ended in October 2019. The Optimal project involved a consortium of partners as follows: Norway - Blue Competence Centre and Guri Kunna VET School, Hitra and Froya Belgium - Federation of European Aquaculture Producers (FEAP) Scotland - Pisces Learning Innovations (PLI) Ltd Ireland - Teachers’ Union of Ireland (TUI). The partnership investigated the delivery of VET to work-based learners through the application of …

Calculating Restaurant Failure Rates Using Longitudinal Census Data, J. J. Healy, Máirtín Mac Con Iomaire

Articles

Failure rates in the restaurant industry are popularly perceived to be far higher than they actually are. This paper calculates failure rates in the Irish Food and Drinks Sector (IFDS), for the first time, using longitudinal census data from the Central Statistics Office (CSO) in Ireland, which follows the European statistical classification of economic activity (NACE). The results are compared with previously published literature on restaurant failure rates in the United States of America. This study also compares IFDS failure rates with other industry sectors in Ireland (construction, manufacturing). Drawing on Stinchcombe’s ’liability of newness’ theory, the informal fallacies theory …

Automatic Misogyny Detection In Social Media: A Survey, John Cardiff, Elena Shushkevich

Conference Papers

This article presents a survey of automated misogyny identification techniques in social media, especially in Twitter. This problem is urgent because of the high speed at which messages on social platforms grow and the widespread use of offensive language (including misogynistic language) in them. In this article we survey approaches proposed in the literature to solve the problem of misogynistic message recognition. These include classical machine learning models like Sup-port Vector Machine, Naive Bayes, Logistic Regression and ensembles of different classical machine learning models and deep neural networks such as Long Short-term memory and Convolutional Neural Networks. We consider results …

Analyzing Twitter Feeds To Facilitate Crises Informatics And Disaster Response During Mass Emergencies, Arshdeep Kaur

Dissertations

It is a common practice these days for general public to use various micro-blogging platforms, predominantly Twitter, to share ideas, opinions and information about things and life. Twitter is also being increasingly used as a popular source of information sharing during natural disasters and mass emergencies to update and communicate the extent of the geographic phenomena, report the affected population and casualties, request or provide volunteering services and to share the status of disaster recovery process initiated by humanitarian-aid and disaster-management organizations. Recent research in this area has affirmed the potential use of such social media data for various disaster …

English Wikipedia Corpus Chunks, Filip Klubicka, Alfredo Maldonado, Abhijit Mahalunkar, John D. Kelleher

Datasets

This archive contains a collection of language corpora. These are text files that contain samples of text collected from English Wikipedia.

English Wordnet Random Walk Pseudo-Corpora, Filip Klubicka, Alfredo Maldonado, Abhijit Mahalunkar, John D. Kelleher

Datasets

This archive contains a collection of pseudo-corpora. These are text files that contain pseudo-sentences generated artificially from a random walk over the English WordNet taxonomy.

Test: A Terminology Extraction System For Technology Related Terms, Murhaf Hossari, Soumyabrata Dev, John Kelleher

Conference papers

Tracking developments in the highly dynamic data-technology landscape are vital to keeping up with novel technologies and tools, in the various areas of Artificial Intelligence (AI). However, It is difficult to keep track of all the relevant technology keywords. In this paper, we propose a novel system that addresses this problem. This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies. The extracted new terms can be logged as new AI technologies as they are found on-the-fly in the web. It can be subsequently classified …

Ex-Post Service Contract Performance Management, Brian Forbes, Malcolm Brady

Articles

This paper highlights how contract incompleteness can threaten the performance of public procurement facilities management contracts during their implementation stages, based on a multiple case study comprising five public procurement services contracts. The paper takes a principle-agent view and with the unit of analysis being the contract itself. The paper shows that contract contingencies are almost inevitable and may stem from the written contract or from the participating organisations. Written and unwritten contract management mechanisms were used in practice to deal with contingencies as they arose in the services case studies examined. The paper found that written contracts do not …

Expanding University Entrepreneurial Ecosystems To Under-Represented Communities, Emma O'Brien, Thomas M. Cooney, Per Blenker

Articles

Purpose – Entrepreneurship education has moved from an elitist view focussing on a start-up and picking-the-winners philosophy towards a broader enterprising behaviour approach; recognising entrepreneurship as an activity of relevance for everybody. The purpose of this paper is to extend this development and identify how university entrepreneurial ecosystems can be expanded to support communities that are under-represented in entrepreneurship.

Design/methodology/approach – Based on an integrative literature review (Torraco, 2005), this paper draws together and synthesises literature from the field of entrepreneurship, higher education studies and under-represented communities in an integrated fashion, leading to the development of a new conceptual model. …

Bigger Versus Similar: Selecting A Background Corpus For First Story Detection Based On Distributional Similarity, Fei Wang, Robert J. Ross, John D. Kelleher

Articles

The current state of the art for First Story Detection (FSD) are nearest neighbourbased models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus. Consequently, the ideal background corpus should arguably be both large-scale to ensure adequate term coverage, and similar to the target domain in terms of the language distribution. However, given these two factors cannot always be mutually satisfied, in this paper we examine whether the distributional similarity of common terms is more important than the scale …

Large-Scale Green Supplier Selection Approach Under A Q-Rung Interval-Valued Orthopair Fuzzy Environment, Limei Liu, Wenzhi Cao, Biao Shi, Ming Tang

Articles

As enterprises pay more and more attention to environmental issues, the green supply chain management (GSCM) mode has been extensively utilized to guarantee profit and sustainable development. Greensupplierselection(GSS),whichisakeysegmentofGSCM,hasbeeninvestigated to put forward plenty of GSS approaches.

Languages For Different Health Information Readers: Multitrait-Multimethod Content Analysis Of Cochrane Systematic Reviews Textual Summary Formats, Jasna Karačić, Pierpaolo Dondio, Ivan Buljan, Darko Hren, Ana Marušić

Articles

Background: Although subjective expressions and linguistic fluency have been shown as important factors in processing and interpreting textual facts, analyses of these traits in textual health information for different audiences are lacking. We analyzed the readability and linguistic psychological and emotional characteristics of different textual summary formats of Cochrane systematic reviews. Methods: We performed a multitrait-multimethod cross-sectional study of Press releases available at Cochrane web site (n= 162) and corresponding Scientific abstracts (n= 158), Cochrane Clinical Answers (n= 35) and Plain language summaries in English (n= 156), French (n= 101), German (n= 41) and Croatian (n=156). We used SMOG index …

On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher

Articles

This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and …

A Qualitative Investigation Of The Degree Of Explainability Of Defeasible Argumentation And Non-Monotonic Fuzzy Reasoning, Lucas Rizzo, Luca Longo

Conference papers

Defeasible argumentation has advanced as a solid theoretical research discipline for inference under uncertainty. Scholars have predominantly focused on the construction of argument-based models for demonstrating non-monotonic reasoning adopting the notions of arguments and conflicts. However, they have marginally attempted to examine the degree of explainability that this approach can offer to explain inferences to humans in real-world applications. Model explanations are extremely important in areas such as medical diagnosis because they can increase human trustworthiness towards automatic inferences. In this research, the inferential processes of defeasible argumentation and non-monotonic fuzzy reasoning are meticulously described, exploited and qualitatively compared. A …

Exploring Online Novelty Detection Using First Story Detection Models, Fei Wang, Robert J. Ross, John D. Kelleher

Conference papers

Online novelty detection is an important technology in understanding and exploiting streaming data. One application of online novelty detection is First Story Detection (FSD) which attempts to find the very first story about a new topic, e.g. the first news report discussing the “Beast from the East” hitting Ireland. Although hundreds of FSD models have been developed, the vast majority of these only aim at improving the performance of the detection for some specific dataset, and very few focus on the insight of novelty itself. We believe that online novelty detection, framed as an unsupervised learning problem, always requires a …

A Comparative Study Of Defeasible Argumentation And Non-Monotonic Fuzzy Reasoning For Elderly Survival Prediction Using Biomarkers, Lucas Rizzo, Ljiljana Majnaric, Luca Longo

Conference papers

Computational argumentation has been gaining momentum as a solid theoretical research discipline for inference under uncertainty with incomplete and contradicting knowledge. However, its practical counterpart is underdeveloped, with a lack of studies focused on the investigation of its impact in real-world settings and with real knowledge. In this study, computational argumentation is compared against non-monotonic fuzzy reasoning and evaluated in the domain of biological markers for the prediction of mortality in an elderly population. Different non-monotonic argument-based models and fuzzy reasoning models have been designed using an extensive knowledge base gathered from an expert in the field. An analysis of …

A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher

Conference papers

Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality …

Great With Child: A Review Of 'Writers And Their Mothers' Edited By Dale Salwak, Palgrave Macmillan, 2018, Sue Norton

Other Resources

No abstract provided.

Non-Manual Articulators In Irish Sign Language Verbs: An Analysis With Data Mining Association Rules, Robert G. Smith, Markus Hofmann

Conference Papers

The Signs of Ireland (SOI) corpus (Leeson et al., 2006) deploys a complex multi-tiered temporal data structure. The process of manually analyzing such data is laborious, cannot eliminate bias and often, important patterns can go completely unnoticed. In addition to this, as a result of the complex nature of grammatical structures contained in the corpus, identifying complex linguistic associations or patterns across tiers is simply too intricate a task for a human to carry out in an acceptable timeframe. This work explores the application of data mining techniques on a set of multi-tiered temporal data from the SOI corpus. Building …

Opinion Mining On Small And Noisy Samples Of Health-Related Texts, John Cardiff, Liliya Akhtyamova, Mikhail Alexandrov, Oleksiy Koshulko

Conference Papers

The topic of people’s health has always attracted the attention of public and private structures, the patients themselves and, therefore, researchers.

Social networks provide an immense amount of data for analysis of health- related issues; however it is not always the case that researchers have enough

data to build sophisticated models. In the paper, we artificially create this lim- itation to test performance and stability of different popular algorithms on small

samples of texts. There are two specificities in this research apart from the size of a sample: (a) here, instead of usual 5-star classification, we use combined classes reflecting …

Extracting Drug-Drug Interactions With Character-Level And Dependency-Based Embeddings, John Cardiff, Liliya Akhtyamova

Conference Papers

The DDI track of TAC-2018 challenge addresses the problem of an information retrieval of drug-drug interactions on structured product labeling documents with discontinuous and overlapping entities. In this paper, we present our participation for event extraction subtask (Task 1). We used a supervised long-short-term memory (LSTM) network with conditional random fields decoding (LSTM-CRF) approach with an automatic exploring of words and characters features. Additional dependency-based information was integrated into word embeddings to allow better word representation. Our system performed with above median score.

School Of Culinary Arts & Food Technology - Autumn Newsletter 2018, James Peter Murphy

Other resources

The School of Culinary Arts and Food Technology, Autumn Newsletter captured the many events, research, awards, significant contributions and special civic and community activities which the students and staff members of the school have successfully completed up to the Autumn period of 2018. The successful completion of these activities would not be possible without the active and on-going support of the 'INSPIRED' friends of Culinary Arts (school sponsors).

Hr500k – A Reference Training Corpus Of Croatian., Nikola Ljubešić, Željko Agić, Filip Klubicka, Vuk Batanović, Tomaž Erjavec

Conference papers

In this paper we present hr500k, a Croatian reference training corpus of 500 thousand tokens, segmented at document, sentence and word level, and annotated for morphosyntax, lemmas, dependency syntax, named entities, and semantic roles. We present each annotation layer via basic label statistics and describe the final encoding of the resource in CoNLL and TEI formats. We also give a description of the rather turbulent history of the resource and give insights into the topic and genre distribution in the corpus. Finally, we discuss further enrichments of the corpus with additional layers, which are already underway.

Design Patterns As A Collaborative Entity Within The Smart Environment, Aitor Arribas Velasco, John Mcgrory, Damon Berry

Conference papers

Modern technology is increasingly being employed to create a “smart” living experience. These “smart” technology entities are producing copious of amounts data, which in turn rely on increased storage, distribution and computation needs to manage the data. Depending on the scenario, the diversity of piecemeal solutions almost mirrors the numbers of problems posed. Some successful solutions touted as being “smart” for example: save energy, or to support in assisted living, have been created, but the true underlying pattern of interactivity has not been identified. In the field of computing, patterns can provide a general, reusable solution to commonly repeatable occurring …

Building Classifiers With Gmdh For Health Social Networks (Bd Askapatient), John Cardiff, Liliya Akhtyamova, Mikhail Alexandrov

Conference Papers

Health social media offer useful data for patients and doctors concerning both various medicines and treatments. Usually, these data are accompanied by their assessments in 5- star scale. But such a detail classification has small usefulness because patients and doctors, first of all, want to know about negative cases and to study in detail the extreme ones. In the paper we build classifiers of texts just for these cases using combined classes as negative, all others and worst, satisfactory, best. For this, we study possibilities of different GMDH-based algorithms and compare them with the results of other methods. The selection …

Classifying Misogynistic Tweets Using A Blended Model: The Ami Shared Task In Ibereval 2018, John Cardiff, Elena Shushkevich

Conference Papers

This article describes a possible solution for Automatic Misogyny Identification (AMI) Shared Task at IBEREVAL-2018. The proposed technique is based on combining several simpler classifiers into one more complex blended model, which classified the data taking into account the probabilities of belonging to classes calculated by simpler models. We used the Logistic Regression, Naive Bayes, and SVM classifiers. The experimental results show that blended model works better than simpler models for all three type of classification, for both binomial classification (Misogyny Identifivation, Target Classification) and multinomial classification (Misogynistic Behavior).

Entity-Grounded Image Captioning, Annika Lindh, Robert J. Ross, John D. Kelleher

Conference papers

An urgent limitation in current Image Captioning models is their tendency to produce generic captions that avoid the interesting detail which makes each image unique. To address this limitation, we propose an approach that enforces a stronger alignment between image regions and specific segments of text. The model architecture is composed of a visual region proposer, a region-order planner and a region-guided caption generator. The region-guided caption generator incorporates a novel information gate which allows visual and textual input of different frequencies and dimensionalities in a Recurrent Neural Network.