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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

21st International Conference (ICCBR 2013) Saratoga Springs, New York, USA, 8 - 11 July, 2013 Case-based reasoning (CBR) attempts to reuse past experiences to solve new problems. CBR ideas are commonplace in recommendation systems, which rely on the similarity between product queries and a case base of product cases. But, the relationship between CBR and many of these recommenders can be tenuous: the idea that product cases made up of static meta-data type features are experiential is a stretch; unless one views the type of case descriptions used by collaborative filtering (user ratings across products) as experiential. Here we explore and evaluate how to automatically generate product cases from user-generated reviews to produce cases that are based on genuine user experiences for use in a case-based product recommendation system. Science Foundation Ireland

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

Featuring the many castles of Limerick and County.

The aim of the lesson is to introduce students to an Irish marine historical personality called John Phillip Holland, who was responsible for influencing the design of submarines. By designing their own DIY submarine, students will also learn about boats that can sink and float. Funder: Marine Institute

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