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International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020), Lisbon, Portugal (held online due to coronavirus outbreak) 14 April 2020 Algorithmic bias has the capacity to amplify and perpetuate societal bias, and presents profound ethical implications for society. Gender bias in algorithms has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning, and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning. The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact in the context of search and recommender systems. Irish Research Council Science Foundation Ireland

6th International Conference on Cloud Computing and Services Science, Rome, Italy, 23-25 April 2016 Information systems and computing capabilities are delivered through the Internet in the form of services; they are regulated by a Service Level Agreement (SLA) contract co-signed by a generic Application Service Provider (ASP) and the end user(s), as happens for instance in the cloud. In such a type of contract several clauses are established; they concern the level of the services to guarantee, also known as quality of service (QoS) parameters, and the penalties to apply in case the requirements are not met during the SLA validity time, among others. SLA contracts use legal jargon, indeed they have legal validity in case of court litigation between the parties. A dedicated contract management facility should be part of the service provisioning because of the contractual importance and contents. Some work in literature about these facilities rely on a structured language representation of SLAs in order to make them machine-readable. The majority of these languages are the result of private stipulation between private industries and not available for public services where SLAs are expressed in common natural language instead. In order to automate the SLAs management, the first step is to recognise the documents. In this paper an investigation towards SLAs text recognition is presented; the proposal is driven by an analysis of the contractual contents necessary to be automatically extracted in order to facilitate possible criminal investigations.

The 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15), Santiago, Chile, 9-13 August 2015 We propose a novel approach for measuring the similarity between weaving patterns that can provide similarity-based search functionality for textile archives. We represent textile structures using hypergraphs and extract multisets of $k$-neighborhoods from these graphs. The resulting multisets are then compared using Jaccard coefficients, Hamming distances, and cosine measures. We evaluate the different variants of our similarity measure experimentally, showing that it can be implemented efficiently and illustrating its quality using it to cluster and query a data set containing more than a thousand textile samples.

A collection of tunes in the Irish traditional idiom composed by Adèle Commins and Daithí Kearney.

The 9th International Conference on e-Health, Lisbon, Portugal, 20-22 July 2017 Recent years have seen the rapid increase in digitised medical information. In particular, the massive expansion of Electronic Health Records (EHRs), which are designed to document all information that is clinically relevant in a patient's use of a healthcare facility, has introduced unprecedented volumes of relatively unstructured data. This paper intends to determine the extent to which knowledge discovery in relation to both abbreviations and acronyms within heterogeneous data can be achieved. Heterogeneous data such as the narrative-based free-text notes found within patients' EHRs may use inconsistent ways to indicate contractions within the text and may use non-standard definitions for both abbreviations and acronyms. We approached this task through the retrieval and classification of contractions as well as using a novel method of combining multiple publically available repositories. In order to provide better coverage of abbreviations, and also to address the issue of neologisms in general, word embeddings were applied to find semantically similar lexemes. Science Foundation Ireland Carlow Emergency Doctors On Call – CAREDOC

The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded, allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations. This work is in part funded by the SSIX Horizon 2020 project (grant agreement No 645425). Vivian S. Silva is a CNPq Fellow – Brazil. peer-reviewed

iConference 2019, Washington, United States of America, March 31- 3 April 2019 We investigate how the term “business model" was used in the digital cultural heritage literature from 2000 to 2015 through content analysis. We found that discussion of business models is not prevalent and there is no observable growth trend. Analysis of how authors represented business models showed predominately positive uses of the concept but include discussion of tension between the concept of business model and traditional cultural heritage field values. We found that non- element representations of business models were most common. Alfred P. Sloan Foundation

The 20th INQUA Congress, Dublin, Ireland, 25-31 July 2019 Glendalough is one of Ireland’s most iconic landscapes, combining stunning scenery with evocative ruined architecture, including distinctively Irish styles such as the round tower. The popular understanding of the valley’s history is that Saint Kevin retreated into the wilderness where he could be closer to God, and that there he founded his monastery which rose to a position of pre-dominance before subsequent decline. This is a powerful story, appealing to important myths about the nature of early Irish Christianity and with a complex relationship with Irish cultural nationalism. However, it is only a partial understanding of the long-term history of how humans have settled the spectacular valley of Glendalough. Glendalough is also often viewed as a natural landscape, but its form is an outcome of the long-term interaction between people and their environment. This brief outline, and fieldtrip, offers a more holistic perspective on this remarkable landscape. Wicklow County Council

Commissioned by the Oriel Traditional Orchestra with funding from the Cooperation with Northern Ireland Funding Scheme 2018 administered by the Department of Culture, Heritage and the Gaeltacht. Inspired by the Oriel region, The Oriel March is for Violin (in two parts), Viola, Cello, Flute, Tin Whistle, Banjo, Mandolin, Harp, Button Accordion, Piano Accordion, Concertina, Uilleann Pipes, Guitar, Piano, Percussion. The five movements are subtitled: 1. MARCHING FROM MAGH MUIRTHEMHNE 2. GOLDEN HOSTAGES 3. GLEANN NA bhFIACH 4. THE JUMPNIG CHURCH 5. ADVANCING ARMIES 5. DAITHÍ'S REEL © 2018 Daithí Kearney, Creative Arts Research Centre at Dundalk Institute of Technology. Performed and recorded under licence by the Oriel Traditional Orchestra. Work developed, under commission, by the Creative Arts Research Centre at Dundalk Institute of Technology.

The Baudelaire Song Project aims to research all the song settings ever of poems by famous French poet Charles Baudelaire, building a pioneering digital dataset which brings together for the first time both pop music and classical music settings of Baudelaire’s verse and prose poetry, totalling 200+ poem texts. I speak with Prof. Helen Abbott and Dr Caroline Ardrey about the project, and what it means for the ways in which we examine literature and music.