14,360 research outputs found

    A systematic literature review on information systems for disaster management and proposals for its future research agenda

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    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

    Coloniality and the Courtroom: Understanding Pre-trial Judicial Decision Making in Brazil

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    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 ‘decolonize the criminal question’ 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 judges’ 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 ‘anticitizen’. The concept builds on Souza’s (2007) ‘subcitizen’ 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 – the normative approach to influencing judicial decision making around pre-trial detention – 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)

    Embodying entrepreneurship: everyday practices, processes and routines in a technology incubator

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    The growing interest in the processes and practices of entrepreneurship has been dominated by a consideration of temporality. Through a thirty-six-month ethnography of a technology incubator, this thesis contributes to extant understanding by exploring the effect of space. The first paper explores how class structures from the surrounding city have appropriated entrepreneurship within the incubator. The second paper adopts a more explicitly spatial analysis to reveal how the use of space influences a common understanding of entrepreneurship. The final paper looks more closely at the entrepreneurs within the incubator and how they use visual symbols to develop their identity. Taken together, the three papers reject the notion of entrepreneurship as a primarily economic endeavour as articulated through commonly understood language and propose entrepreneuring as an enigmatic attractor that is accessed through the ambiguity of the non-verbal to develop the ‘new’. The thesis therefore contributes to the understanding of entrepreneurship and proposes a distinct role for the non-verbal in that understanding

    Grounded in Relationships of Support: Indigenous Teacher Mentorship in the Rural West

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    This article explores the power of Indigenous teacher mentorship as essential to address “the change in point of view” long called for in Indigenous education. Drawing from a longitudinal, ethnographic study of an Indigenous teacher education program in a predominantly rural, high need region, we examine the basic questions: What do Indigenous master teachers uniquely bring to teacher education? In what ways do Indigenous master teachers support the development of socially, culturally, linguistically, and place-responsive teachers? Using the theoretical frameworks of Tribal Critical Race Theory (TribalCrit) and situated learning, our findings elucidate the importance of Indigenous mentorship for re-membering and re-claiming Indigenous epistemologies, ontologies, and axiologies in relational and intergenerational learning—practices that interrupt coloniality in teacher education and school leadership. Discussion of Indigenous teacher mentorship centers the importance of relationships between people and place in teaching and learning and asks educators and school leaders to conceptualize Indigenous teacher education as a long-term project of tribal nation building and community wellbeing

    Brain simulation as a cloud service: The Virtual Brain on EBRAINS

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    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

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    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 grĂ¶ĂŸere 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€ĂŸigen Gittern auf allgemeine (unregelmĂ€ĂŸige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten ĂŒber EntitĂ€ten in maschinenlesbaren Form. Obwohl große 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 ÜberprĂŒfung und Erweiterung der Graphen wird aufgrund der großen 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 groß 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. Außerdem 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. Schließlich 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

    Politically Engaged Wild Animals

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    My dissertation is called Politically Engaged Wild Animals; in it, I suggest that wild animals live in a politicized world, which gives their behaviour unintended political meanings—if humans will listen appropriately. To arrive at this conclusion, I start with Dinesh Wadiwel's (2015) biopower critique according to which any proposals to conserve wilderness or protect wild animals, which relies on human representatives, suffer from a particular sort of risk, namely that of transforming the current overt domination into a neoliberal form of continued human supremacy. I find this critique has traction against proposals like Sue Donaldson and Will Kymlicka's (2011) suggestion of Wild Animal Sovereignty. However, it has less traction against Anishinaabe (Indigenous) legal traditions, which prioritize respect for wild animals and the ecosystems we share with them. In these legal systems, wild animals are not under the jurisdiction of human societies; they are in independent communities that are part of a shared interspecies world. Thinking of wild animal communities as independently entitled to share the land, water, and air with humans can highlight the interspecies political meanings of conflict between human society and wild animal communities. I suggest we can listen to the behaviour patterns of wild animal communities to reveal unintended political meaning (e.g. protesting human activity or negotiating boundaries with neighbouring groups). Further, researchers are already developing ways to better communicate with wild animals so we all might safely share contested spaces. The moral-political implications of these research projects are typically left in anthropocentric terms. In my view we should reframe this communication as a way of respecting the relationships we already have with wild animal communities. If we can communicate with them, negotiating mutually beneficial boundaries, then we ought to take this communication as a form of political participation. Wild animal communities are speaking for themselves, on land they share with humans, in a politicized world

    A productive response to legacy system petrification

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    Requirements change. The requirements of a legacy information system change, often in unanticipated ways, and at a more rapid pace than the rate at which the information system itself can be evolved to support them. The capabilities of a legacy system progressively fall further and further behind their evolving requirements, in a degrading process termed petrification. As systems petrify, they deliver diminishing business value, hamper business effectiveness, and drain organisational resources. To address legacy systems, the first challenge is to understand how to shed their resistance to tracking requirements change. The second challenge is to ensure that a newly adaptable system never again petrifies into a change resistant legacy system. This thesis addresses both challenges. The approach outlined herein is underpinned by an agile migration process - termed Productive Migration - that homes in upon the specific causes of petrification within each particular legacy system and provides guidance upon how to address them. That guidance comes in part from a personalised catalogue of petrifying patterns, which capture recurring themes underlying petrification. These steer us to the problems actually present in a given legacy system, and lead us to suitable antidote productive patterns via which we can deal with those problems one by one. To prevent newly adaptable systems from again degrading into legacy systems, we appeal to a follow-on process, termed Productive Evolution, which embraces and keeps pace with change rather than resisting and falling behind it. Productive Evolution teaches us to be vigilant against signs of system petrification and helps us to nip them in the bud. The aim is to nurture systems that remain supportive of the business, that are adaptable in step with ongoing requirements change, and that continue to retain their value as significant business assets
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