On a Question of Kulshammer for Representations of Finite Groups in Reductive Groups

Peer reviewed

On a Question of Kulshammer for Representations of Finite Groups in Reductive Groups

Peer reviewed

"On our way": identities and representations of African women seeking asylum in Ireland

This thesis provides the first explicit Postcolonial study of asylum in the Irish context that integrates Black Feminist analyses of intersectional identity with Postcolonial Feminist theories of representation. African women seeking asylum in the Republic of Ireland were key political instruments used by the state to re-draw racial lines. The study examines how, for a group of African women “On their Way” through asylum, identity and representation work hand in hand to force identities, subaltern spaces and bodies to occupy them. Rich biographical data is gathered through mixed art and drama methods over two intensive participatory research projects conducted in a small Irish city. Data analysis critically examines the poetics (practices that signify) and politics (the powers that govern these practices) and affective economies of global and local NGO visual representations, exposing how they consume, fragment, and appropriate African women’s identities and bodies. Though hypervisible, the women themselves “cannot speak”. The women in the study reported feeling “tired” and “used”. Asking “What work are they doing as they do asylum?” the study finds that black female identities and bodies are forced to perform political, cultural, emotional and material labour on their way through this context of Irish asylum. The author argues that Postcolonial Asylum is a performative encounter that re-scripts colonial race/class/gender discourse through a humanitarian alibi to naturalize European/white supremacy, reinscribe patriarchal power and justify racialised incarceration of bodies seeking asylum in the North. This study takes an interdisciplinary approach that centralizes Black and Postcolonial Feminist theory and innovates Participatory Art-Based Action methodology. Black and Postcolonial feminisms can recognize, theorize and replenish black female political and intellectual agency. Participatory Action research, if grounded in Black feminist epistemology and ethics, can allow participants to “speak back” to what is already said about them in spaces of convivial self-representation.

Magners Man: Irish Cider, Representations of Masculinity and the "Burning Celtic Soul"

In this paper we go in search of the Celtic Soul, tracking its historical intertwining with, and relation to, Irish masculinity, from Ireland's pre-colonial past to its colonial days and finally to its postcolonial present. We argue that the Celtic soul manifests itself, with great success, in the Magners Irish Cider advertising campaign. As a key part of our analysis we also illustrate how representations of the Irish Celt serve as a means of enabling young male consumers to reconcile the many tensions and contradictions they are experiencing over what it means to perform ideals of masculinity in contemporary western culture.

The user's view level of the GOQL graphical query language

The paper addresses issues related to the design of a graphical query mechanism that can act as an interface to any object-oriented database system (OODBS), in general, and the object model of ODMG 2.0, in particular. In the paper a brief literature survey of related work is given, and an analysis methodology that allows the evaluation of such languages is proposed. Moreover, the user's view level of a new graphical query language, namely GOQL (Graphical Object Query Language), for ODMG 2.0 is presented. The user's view level provides a graphical schema that does not contain any of the perplexing details of an object-oriented database schema, and it also provides a foundation for a graphical interface that can support ad-hoc queries for object-oriented database applications. We illustrate, using an example, the user's view level of GOQL

Gender, Nationality and Cultural Representations of Ireland: An Irish Woman's Place?

Ireland has struggled with its ‘feminine’ identity throughout its history. The so-called ‘chasmic dichotomy of male and female' is embedded in colonial and postcolonial constructions of Irishness and it continues to manifest itself in contemporary cultural representations of Ireland and Irishness. This study explores issues of gender and nationality via a reading of a 70-second television advertisement for Caffrey's Irish Ale, titled ‘New York’. The article suggests that, although colonial and postcolonial discourse on Ireland continues to perceive the ‘feminine’ in problematic terms, this is gradually changing as Irish women increasingly, in poet Eavan Boland's words, ‘open a window on those silences, those false pastorals, those ornamental reductions’ that have confined us.

Créolization in a Caribbean Landscape: : Representations of Deteriorating Landscapes and Internal Exile in Edwidge Danticat's Claire of the Sea Light

engelska

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.

Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics / researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.

Structure learning of context-specific graphical models

Abstract The ultimate problem considered in this thesis is modeling a high-dimensional joint distribution over a set of discrete variables. For this purpose, we consider classes of context-specific graphical models and the main emphasis is on learning the structure of such models from data. Traditional graphical models compactly represent a joint distribution through a factorization justi ed by statements of conditional independence which are encoded by a graph structure. Context-speci c independence is a natural generalization of conditional independence that only holds in a certain context, speci ed by the conditioning variables. We introduce context-speci c generalizations of both Bayesian networks and Markov networks by including statements of context-specific independence which can be encoded as a part of the model structures. For the purpose of learning context-speci c model structures from data, we derive score functions, based on results from Bayesian statistics, by which the plausibility of a structure is assessed. To identify high-scoring structures, we construct stochastic and deterministic search algorithms designed to exploit the structural decomposition of our score functions. Numerical experiments on synthetic and real-world data show that the increased exibility of context-specific structures can more accurately emulate the dependence structure among the variables and thereby improve the predictive accuracy of the models.