15,249 research outputs found
Serving to secure "Global Korea": Gender, mobility, and flight attendant labor migrants
This dissertation is an ethnography of mobility and modernity in contemporary South Korea (the Republic of Korea) following neoliberal restructuring precipitated by the Asian Financial Crisis (1997). It focuses on how comparative âservice,â âsecurity,â and âsafetyâ fashioned âGlobal Koreaâ: an ongoing state-sponsored project aimed at promoting the economic, political, and cultural maturation of South Korea from a once notoriously inhospitable, âbackwardâ country (hujinâguk) to a now welcoming, âadvanced countryâ (sĆnjinâguk). Through physical embodiments of the culturally-specific idiom of âsuperiorâ service (sĆbisĆ), I argue that aspiring, current, and former Korean flight attendants have driven the production and maintenance of this national project.
More broadly, as a driver of this national project, this occupation has emerged out of the countryâs own aspirational flights from an earlier history of authoritarian rule, labor violence, and xenophobia. Against the backdrop of the Korean stateâs aggressive neoliberal restructuring, globalization efforts, and current âHell Chosunâ (HelchosĆn) economy, a group of largely academically and/or class disadvantaged young women have been able secure individualized modes of pleasure, self-fulfillment, and class advancement via what I deem âservice mobilities.â Service mobilities refers to the participation of mostly women in a traditionally devalued but growing sector of the global labor market, the âpink collarâ economy centered around âfeminineâ care labor. Korean female flight attendants share labor skills resembling those of other foreign labor migrants (chiefly from the âGlobal Southâ), who perform care work deemed less desirable. Yet, Korean female flight attendants elude the stigmatizing, classed, and racialized category of âlabor migrant.â Moreover, within the context of South Koreaâs unique history of rapid modernization, the flight attendant occupation also commands considerable social prestige.
Based on ethnographic and archival research on aspiring, current, and former Korean flight attendants, this dissertation asks how these unique care laborers negotiate a metaphorical and literal series of sustained border crossings and inspections between Korean flight attendantsâ contingent status as lowly care-laboring migrants, on the one hand, and ostensibly glamorous, globetrotting elites, on the other. This study contends the following: first, the flight attendant occupation in South Korea represents new politics of pleasure and pain in contemporary East Asia. Second, Korean female flight attendantsâ enactments of soft, sanitized, and glamorous (hwaryĆhada) service help to purify South Koreaâs less savory past. In so doing, Korean flight attendants reconstitute the historical role of female laborers as burden bearers and caretakers of the Korean state.U of I OnlyAuthor submitted a 2-year U of I restriction extension request
A systematic literature review on information systems for disaster management and proposals for its future research agenda
Emergency management information systems (EMIS) are fundamental for responding to disasters effectively since they provide and process emergency-related information. A literature stream has emerged that corresponds with the increased relevance of the wide array of different information systems that have been used in response to disasters. In addition, the discussion around systems used primarily within responder organizations broadened to systems such as social media that are open to the general public. However, a systematic review of the EMIS literature stream is still missing. This literature review presents a timeline of EMIS research from 1990 up to 2021. It shows the types of information system scholars focused on, and what disaster response functions they supported. It furthermore identifies challenges in EMIS research and proposes future research directions
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Co-design As Healing: Exploring The Experiences Of Participants Facing Mental Health Problems
This thesis is an exploration of the healing role of co-design in mental health. Although co-design projects conducted within mental health settings are rising, existing literature tends to focus on the object of design and its outcomes while the experiences of participants per se remain largely unexplored. The guiding research question of this study is not how we design things that improve mental health, but how co-designing, as an act, might do so.
The thesis presents two projects that were organized in collaboration with the mental health charity Islington Mind and the Psychosis Therapy Project (PTP) in London.
The project at Islington Mind used a structured design process inviting participants to design for wellbeing. A case study analysis provides insights on how participants were impacted, summarizing key challenges and opportunities.
The design at PTP worked towards creating a collective brief in an emergent fashion, finally culminating in a board game. The experiences of participants were explored through Interpretative Phenomenological Analysis (IPA), using semi-structured interview data. The analysis served to identify key themes characterising the experience of co-design such as contributing, connecting, thinking and intentioning. In addition, a mixed-methods analysis of questionnaires and interview data exploring participants' wellbeing, showed that all participants who engaged fairly consistently in the project improved after the project ended, although some participants' scores returned to baseline six months later.
Reflecting on both projects, an approach to facilitation within mental health is outlined, detailing how the dimensions of weaving and layered participation, nurturing mattering and facilitating attitudes interlace. This contribution raises awareness of tacit dimensions in the practice of facilitation, articulating the nuances of how to encourage and sustain meaningful and ethical engagement and offering insights into a range of tools. It highlights the importance of remaining reflexive in relation to attitudes and emotions and discusses practical methodological and ethical challenges and ways to resolve them which can be of benefit to researchers embarking on a similar journey.
The thesis also offers detailed insights on how methodologies from different fields were integrated into a whole, arguing for transparency and reflexivity about epistemological assumptions, and how underlying paradigms shift in an interdisciplinary context.
Based on the overall findings, the thesis makes a case for considering design as healing (or a designerly way of healing), highlighting implications at a systems, social and individual level. It makes an original contribution to our understanding of design, highlighting its healing character, and proposes a new way to support mental health. The participants in this study not only had increased their own wellbeing through co-designing, but were also empowered and contributed towards healing the world. Hence, the thesis argues for a unique, holistic perspective of design and mental health, recognizing the interconnectedness of the individual, social and systemic dimensions of the healing processes that are ignited
Coloniality and the Courtroom: Understanding Pre-trial Judicial Decision Making in Brazil
This thesis focuses on judicial decision making during custody hearings in Rio de Janeiro, Brazil. The impetus for the study is that while national and international protocols mandate the use of pre-trial detention only as a last resort, judges continue to detain people pre-trial in large numbers. Custody hearings were introduced in 2015, but the initiative has not produced the reduction in pre-trial detention that was hoped. This study aims to understand what informs judicial decision making at this stage. The research is approached through a decolonial lens to foreground legacies of colonialism, overlooked in mainstream criminological scholarship. This is an interview-based study, where key court actors (judges, prosecutors, and public defenders) and subject matter specialists were asked about influences on judicial decision making. Interview data is complemented by non-participatory observation of custody hearings. The research responds directly to Aliverti et al.'s (2021) call to âdecolonize the criminal questionâ by exposing and explaining how colonialism informs criminal justice practices. Answering the call in relation to judicial decision making, findings provide evidence that colonial-era assumptions, dynamics, and hierarchies were evident in the practice of custody hearings and continue to inform judgesâ decisions, thus demonstrating the coloniality of justice. This study is significant for the new empirical data presented and theoretical innovation is also offered via the introduction of the âanticitizenâ. The concept builds on Souzaâs (2007) âsubcitizenâ to account for the active pursuit of dangerous Others by judges casting themselves as crime fighters in a modern moral crusade. The findings point to the limited utility of human rights discourse â the normative approach to influencing judicial decision making around pre-trial detention â as a plurality of conceptualisations compete for dominance. This study has important implications for all actors aiming to reduce pre-trial detention in Brazil because unless underpinning colonial logics are addressed, every innovation risks becoming the next lei para inglĂȘs ver (law [just] for the English to see)
Brain simulation as a cloud service: The Virtual Brain on EBRAINS
open access articleThe Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic con- version of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collabo- ration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation
Machine learning for managing structured and semi-structured data
As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs.
Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs.
In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer gröĂere Datenmengen verfĂŒgbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlĂ€sslich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren ZusammenhĂ€nge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfĂŒgbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmĂ€Ăigen Gittern auf allgemeine (unregelmĂ€Ăige) Graphen betrachtet werden.
Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten ĂŒber EntitĂ€ten in maschinenlesbaren Form. Obwohl groĂe Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollstĂ€ndig, d. h. es fehlen Fakten. Die manuelle ĂberprĂŒfung und Erweiterung der Graphen wird aufgrund der groĂen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstĂŒtzt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der WissensgraphenvervollstĂ€ndigung lĂ€sst sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen EntitĂ€ten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame EntitĂ€ten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknĂŒpfen.
In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der VervollstĂ€ndigung von Wissensgraphen vor. FĂŒr das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, wĂ€hrend die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die LeistungsfĂ€higkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. FĂŒr die Link Prediction demonstrieren wir, wie die Vorhersage fĂŒr unbekannte EntitĂ€ten zur Trainingszeit verbessert werden kann, indem zusĂ€tzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfĂŒgbar sind. GestĂŒtzt auf Ergebnisse einer groĂ angelegten experimentellen Studie prĂ€sentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. AuĂerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugĂ€nglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. SchlieĂlich schlagen wir eine neuartige Metrik fĂŒr die Bewertung von Ranking-Ergebnissen vor, wie sie fĂŒr beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in FĂ€llen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen fĂŒr beide Aufgaben vorkommen
UFO: Unified Foundational Ontology
The Unified Foundational Ontology (UFO) was developed over the last two decades by consistently putting together theories from areas such as formal ontology in philosophy, cognitive science, linguistics, and philosophical logics. It comprises a number of micro-theories addressing fundamental conceptual modeling notions, including entity types and relationship types. The aim of this paper is to summarize the current state of UFO, presenting a formalization of the ontology, along with the analysis of a number of cases to illustrate the application of UFO and facilitate its comparison with other foundational ontologies in this special issue. (The cases originate from the First FOUST Workshop â the Foundational Stance, an international forum dedicated to Foundational Ontology research.
AIUCD 2022 - Proceedings
Lâundicesima edizione del Convegno Nazionale dellâAIUCD-Associazione di Informatica Umanistica ha per titolo Culture digitali. Intersezioni: filosofia, arti, media. Nel titolo Ăš presente, in maniera esplicita, la richiesta di una riflessione, metodologica e teorica, sullâinterrelazione tra tecnologie digitali, scienze dellâinformazione, discipline filosofiche, mondo delle arti e cultural studies
SYSTEMS METHODS FOR ANALYSIS OF HETEROGENEOUS GLIOBLASTOMA DATASETS TOWARDS ELUCIDATION OF INTER-TUMOURAL RESISTANCE PATHWAYS AND NEW THERAPEUTIC TARGETS
In this PhD thesis is described an endeavour to compile litterature about Glioblastoma key molecular mechanisms into a directed network followin Disease Maps standards, analyse its topology and compare results with quantitative analysis of multi-omics datasets in order to investigate Glioblastoma resistance mechanisms. The work also integrated implementation of Data Management good practices and procedures
Pressure training in sport : Factors to enhance design and delivery of applied interventions
This thesis examined how sport psychologists can design and deliver pressure training (PT) to maximise PTâs impact on performance in sport. Adopting a pragmatic approach to research, four studies were conducted to provide practitioners with guidance for conducting PT. The first study was a meta-analysis of previous PT interventions. PT had a moderate positive effect on performance under pressure when compared to training without pressure. Building on Stoker et al.âs (2016) framework for creating pressure, the second study identified properties of pressure manipulations that international-level athletes and sport psychologists had found to be effective. This study also explored the specific benefits of PT that lead to improved performance. In the third study, athletes and sport psychologists also described effective delivery of PT. Key findings included processes such as collaboration and integration of PT into training sessions, and these processes may counter risks that PT could pose to athletesâ wellbeing. The fourth study applied the previous findings to a PT intervention with a professional womenâs basketball team. Results further extended knowledge on creating pressure and delivering PT. Specifically, pressure may be created more effectively through negative, rather than positive, consequences that have meaningful implications for athletes. This study also highlighted that fully integrating PT into training can include coaches reinforcing pressure manipulations and supporting performance under pressure. Additional applied implications of this thesis include PTâs potential to complement mental skills training and the need to distinguish PT from training that simulates other aspects of competition. Future research can investigate the training environments and characteristics of individuals that are conducive to effective PT. More knowledge on creating pressure is especially needed for team sports because of individual differences within a team. Studies can also test the properties of pressure manipulations that were explored qualitatively in this thesis
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