Extending Modular Semantics for Bipolar Weighted Argumentation (Technical Report)

Weighted bipolar argumentation frameworks offer a tool for decision support and social media analysis. Arguments are evaluated by an iterative procedure that takes initial weights and attack and support relations into account. Until recently, convergence of these iterative procedures was not very well understood in cyclic graphs. Mossakowski and Neuhaus recently introduced a unification of different approaches and proved first convergence and divergence results. We build up on this work, simplify and generalize convergence results and complement them with runtime guarantees. As it turns out, there is a tradeoff between semantics' convergence guarantees and their ability to move strength values away from the initial weights. We demonstrate that divergence problems can be avoided without this tradeoff by continuizing semantics. Semantically, we extend the framework with a Duality property that assures a symmetric impact of attack and support relations. We also present a Java implementation of modular semantics and explain the practical usefulness of the theoretical ideas

Teaching the YouTube generation: exploring the benefits of an interactive teaching approach in sustainable product design

This paper presents findings from a doctoral study, which investigated effective methods for teaching social sustainability within product design courses in British and Irish universities. Specifically exploring, how to foster a holistic understanding of the social aspects of sustainable product design amongst undergraduate and postgraduate students, through design thinking. Perceived relevance is considered as a fundamental aspect in enabling students to engage deeply with sustainability [1]. Authors [2;3;4] note that 'Net Generation' learners have specific learning preferences that can be targeted in order to improve the students learning experience. Through the careful design of materials which build upon the students tendency towards visual learning and seeking increase relevance and motivation, by offering opportunities for collaborative learning and learning through discovery. Three 'Rethinking Design' workshops were designed and developed as part of a doctoral study to introduce students to the wider social aspects of sustainability and these were conducted in five universities in Britain and Ireland. The workshops featured visually rich audio visual introductions followed by collaborative group based mind mapping activities, which were successful in fostering deep learning by facilitating learning through discovery, critical reflection, peer learning and creativity leading to an exploration of design thinking solutions

Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data

In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the model’s ability to generalise to other data genres

VALUING COMMUNITY DEVELOPMENT THROUGH THE SOCIAL INCLUSION PROGRAMME (SICAP) 2015–2017 TOWARDS A FRAMEWORK FOR EVALUATION. ESRI RESEARCH SERIES NUMBER 77 FEBRUARY 2019

The Social Inclusion and Community Activation Programme (SICAP) represents a major component of Ireland’s community development strategy, led by the Department of Rural and Community Development (DRCD). The vision of SICAP is to improve the opportunities and life chances of those who are marginalised in society, experiencing unemployment or living in poverty through community development approaches, targeted supports and interagency collaboration, where the values of equality and inclusion are promoted and human rights are respected. In 2016, total expenditure on SICAP amounted to approximately €36 million (Pobal, 2016a). Using a mixed methodology, this report examines the extent to which community development programmes can or should be subject to evaluation, with a particular focus on SICAP. In doing so, the report draws on a rich body of information – including desk-based research; consultation workshops with members of local community groups (LCGs), local community workers (LCWs) and other key policy stakeholders; and an analysis of administrative data held by Pobal – on the characteristics of LCGs that received direct support under SICAP. The findings in this report relate to the delivery of the SICAP 2015–2017 programme which ended in December 2017. The aim of the study is to inform policy by shedding light on a number of issues including the following. Can community development be evaluated? What are the current metrics and methodologies suggested in the literature for evaluating community development interventions? What possible metrics can be used to evaluate community development interventions and how do these relate to the SICAP programme? How can a framework be developed that could potentially be used by SICAP for monitoring evaluation of its community development programme