6,431 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    A Hermeneutic Phenomenological Study into International Baccalaureate Diploma Programme Teachers\u27 Lived Experience of Professional Growth

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    The International Baccalaureate (IB) program provides an inquiry- and concept-driven approach to teaching and learning in primary and secondary schools around the world. This educational philosophy is often different to teachers’ previous training and experience, yet little research has been done into how continuing professional development addresses the challenge of understanding and implementing the IB program. The purpose of this hermeneutic phenomenological study was to explore the professional learning experiences of IB Diploma Programme (DP) teachers in international schools. Semi-structured interviews were conducted with seven teachers from five different schools and all six IB subject groups who had severalyears’ experience of teaching in the IB program. Explication of data showed that DP teachers found aspects of official IB workshops to be helpful, but these trainings were insufficient in themselves for the ongoing, job-embedded learning required to understand and implement the IB educational philosophy. While andragogical principles were found to be beneficial in formal learning sessions to guide teacher growth, heutagogical practice, or self-initiated and -directed learning, leads IB teachers to seek out informal professional growth activities that enable them to develop both individually and collectively in their school contexts

    Data-assisted modeling of complex chemical and biological systems

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    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter

    The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures

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    Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation. Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canada’s AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents. In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI

    Jews in East Norse Literature

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    This book explores the portrayal of Jews and Judaism in medieval Danish and Swedish literary and visual culture. Drawing on over 100 manuscripts and incunabula as well as runic inscriptions and religious art, the author describes the various, often contradictory, images ranging from antisemitism and anti-Judaism to the elevation of Jews as morally exemplary figures. It includes new editions of 54 East Norse texts with English translations

    Anwendungen maschinellen Lernens für datengetriebene Prävention auf Populationsebene

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    Healthcare costs are systematically rising, and current therapy-focused healthcare systems are not sustainable in the long run. While disease prevention is a viable instrument for reducing costs and suffering, it requires risk modeling to stratify populations, identify high- risk individuals and enable personalized interventions. In current clinical practice, however, systematic risk stratification is limited: on the one hand, for the vast majority of endpoints, no risk models exist. On the other hand, available models focus on predicting a single disease at a time, rendering predictor collection burdensome. At the same time, the den- sity of individual patient data is constantly increasing. Especially complex data modalities, such as -omics measurements or images, may contain systemic information on future health trajectories relevant for multiple endpoints simultaneously. However, to date, this data is inaccessible for risk modeling as no dedicated methods exist to extract clinically relevant information. This study built on recent advances in machine learning to investigate the ap- plicability of four distinct data modalities not yet leveraged for risk modeling in primary prevention. For each data modality, a neural network-based survival model was developed to extract predictive information, scrutinize performance gains over commonly collected covariates, and pinpoint potential clinical utility. Notably, the developed methodology was able to integrate polygenic risk scores for cardiovascular prevention, outperforming existing approaches and identifying benefiting subpopulations. Investigating NMR metabolomics, the developed methodology allowed the prediction of future disease onset for many common diseases at once, indicating potential applicability as a drop-in replacement for commonly collected covariates. Extending the methodology to phenome-wide risk modeling, elec- tronic health records were found to be a general source of predictive information with high systemic relevance for thousands of endpoints. Assessing retinal fundus photographs, the developed methodology identified diseases where retinal information most impacted health trajectories. In summary, the results demonstrate the capability of neural survival models to integrate complex data modalities for multi-disease risk modeling in primary prevention and illustrate the tremendous potential of machine learning models to disrupt medical practice toward data-driven prevention at population scale.Die Kosten im Gesundheitswesen steigen systematisch und derzeitige therapieorientierte Gesundheitssysteme sind nicht nachhaltig. Angesichts vieler verhinderbarer Krankheiten stellt die Prävention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung für ein präventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und Maßnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da für die meisten Krankheiten keine Risikomodelle existieren und sich verfüg- bare Modelle auf einzelne Krankheiten beschränken. Weil für deren Berechnung jeweils spezielle Sets an Prädiktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe Datenmodalitäten, wie Bilder oder -omics- Messungen, systemische Informationen über zukünftige Gesundheitsverläufe, mit poten- tieller Relevanz für viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch für die Risikomod- ellierung unzugänglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier Datenmodalitäten in der Primärpräven- tion zu untersuchen: polygene Risikoscores für die kardiovaskuläre Prävention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- ität wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenüber üblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der Datenmodalität zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores für die kardiovaskuläre Prävention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen über den zukünftigen Ausbruch von Krankheiten. Unter Einsatz einer phänomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prädik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert für deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, präventionsori- entierten Medizin zu verändern

    Making Connections: A Handbook for Effective Formal Mentoring Programs in Academia

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    This book, Making Connections: A Handbook for Effective Formal Mentoring Programs in Academia, makes a unique and needed contribution to the mentoring field as it focuses solely on mentoring in academia. This handbook is a collaborative institutional effort between Utah State University’s (USU) Empowering Teaching Open Access Book Series and the Mentoring Institute at the University of New Mexico (UNM). This book is available through (a) an e-book through Pressbooks, (b) a downloadable PDF version on USU’s Open Access Book Series website), and (c) a print version available for purchase on the USU Empower Teaching Open Access page, and on Amazon

    Empowering Information Systems Users: The Role of Timely and Customizable Information for User Engagement and Selection Behavior

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    Information systems (IS) increasingly empower their users by strengthening users’ capability and autonomy to make their own decisions how to use and engage with IS. Specifically, users are empowered when they have sufficient knowledge to make rational decisions within IS and sufficient control to shape their experience with IS. In line with these pillars of empowerment, technological advancements unlock new possibilities for IS providers to empower users with access to high quality information (e.g., by providing timely updates of dynamically changing information) and with the ability to control the information stream (e.g., by implementing interfaces to customize websites). As a result, users have greater autonomy to actively shape their user experience to their likening, making them less dependent on having to identify IS that match their needs. At the same time, empowering users pays off for IS providers, as empowered users are known to form more positive attitudes and intentions to engage with the empowering IS. This thesis addresses the two aforementioned pillars of empowerment through knowledge and empowerment through control. Four studies shed light on how the increasingly prevalent practice of empowering users with timely and customizable information affects user engagement as well as users’ selection behavior. The first strand of this thesis investigates user empowerment through timely information in the context of decision support systems (DSS) that aid users in their selection of which (physical) location to visit. To avoid congestion at locations, such DSS communicate how busy each location is by displaying crowding information (CI), accompanied by timeliness cues indicating when this CI was retrieved (e.g., “updated just now” vs. “average over the last year”). Helping users avoid crowded locations becomes all the more important during periods of extraordinary pathogenic risk, such as the COVID-19 pandemic, where physical distancing is imperative for the containment of the pathogen. Against this background, the first study in this thesis investigates how CI with different levels of timeliness affects how users select between differently crowded medical practices. The results demonstrate that while the display of CI is generally useful for users to avoid crowded locations, providing particularly timely CI (i.e., updated close to real-time) leads users to select less crowded locations even more effectively. Moreover, this effect is strongest for individuals who exhibit low levels of health anxiety – an important contextual variable influencing user behavior during the COVID-19 pandemic. The second study extends the findings of the first study by investigating a context in which hedonic motives may encourage users to seek instead of avoid crowds. Specifically, the study examines how timely CI affects users’ choice between differently crowded bars. Despite users longing for the presence of others as part of their visit experience, the results show that particularly timely CI makes users more aware of potential costs of congestion (e.g., prolonged wait times) and consequently leads users to select less crowded locations – thereby corroborating the previous findings in the utilitarian context of selecting a medical practice. Importantly, timelier CI also increases user engagement in that users express a greater intention to reuse the DSS providing the CI. This finding indicates that timely CI not only contributes to the containment of congestion, but also allows DSS providers to retain users more effectively and thereby achieve recurring impact on the reduction of crowding. The second strand of this thesis investigates user empowerment through customizable information in the context of (banner) ads on websites. As ads oftentimes cause irritation and stifle user engagement with the website, first website providers have begun to empower users to customize how many ads they agree to have displayed. Despite website providers hoping to thereby enhance user engagement, it is unclear how users respond to the ability to customize ads they never asked for. Against this backdrop, the third study investigates how the provision of ad quantity customization (AQC) affects user engagement and which ad quantity levels users opt for. The results demonstrate that offering AQC consistently enhances user engagement in that users with access to AQC stay longer on the website and visit more sub-pages than users who cannot customize ad quantity. Counter-intuitively, a website with ads that offers AQC elicits even greater user engagement than a website that is entirely free of ads by default. In addition, the effect on user engagement is strongest for users accessing the website with a mobile (vs. stationary) device. Interestingly, users do not configure AQC to eliminate ads altogether, but instead opt for 29.0% of the default amount of ads to be displayed. The fourth study seeks to extend the previous findings by shedding light on the underlying mechanism that drives the effect of providing AQC on user engagement. The findings suggest that offering AQC elicits perceived empowerment as a pivotal stimulant with two important outcomes: First, users pay closer attention to the website, thereby discovering more information useful to them and consequently experiencing a greater fit between the website’s information and their own needs. Second, the feeling of being in control over ads, as typically immutable and irritating website elements, elicits a sense of enjoyment. Both informational fit and perceived enjoyment then lead users to engage more intensely with the website. Overall, this thesis showcases the role and importance of IS-enabled user empowerment by providing a more comprehensive understanding of how empowering users with timely and customizable information affects user engagement and users’ selection behavior. In doing so, this thesis answers calls for research that urge scholars to not only shed light on emerging phenomena, but also to enable and empower IS users. The studies in this thesis contribute to IS research on empowerment by (1) revealing the importance of timeliness of information as a thus far under-investigated source of empowerment and by (2) uncovering ad customization as a hitherto largely neglected, yet important piece of web customization that complements our understanding of empowerment mechanisms. In addition, this thesis also offers valuable insights and actionable recommendations how DSS providers and policy makers can harness empowerment through timely CI to recurringly reduce crowding without infringing on users’ freedom. Likewise, this thesis guides website providers how to leverage ads as website elements that users enjoy to customize to boost user engagement with the website as a whole

    IMPROVING POPULATION HEALTH BY ADDRESSING SOCIAL DETERMINANTS OF MENTAL HEALTH

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    This study examined the social determinants of mental health as influential factors on health outcomes. Three research studies comprised the dissertation. The first study was a systematic review that identified factors linking common mental disorders to the incidence of the four most prevalent non-communicable diseases (NCDs). Interventions to prevent poor health should target smokers, the elderly, women, and individuals with fewer than 12 years of schooling, according to findings. The second mixed-method study found that the pandemic and its control measures negatively impacted social determinants of mental health and health outcomes, with women, children and informal workers in Gaza being most affected. Some of the strategies deployed by the United Nations for the Relief and Works Agency in the Near East (UNRWA), such as the use of telemedicine, warrant further investigation for efficiency and acceptability. The third study assessed UNRWA's mental health and psychosocial support (MHPSS) response addressing the social determinants of mental health during the COVID-19 pandemic. During Group Model Building (GMB) workshops, participants shared their perspectives on what UNRWA did and how it addressed the vulnerabilities of Palestine refugees in Gaza during the health crisis. Findings suggested improving community wellbeing and enhancing staff support for better future pandemic preparedness. The PhD concludes that addressing social determinants of mental health is a joint responsibility between state and non-state actors and that it is necessary to reduce health inequities to lessen the global burden of disease. In addition to rigorous testing and contact tracing, addressing these determinants during crises, for example by distributing financial aid to poor families and strengthening social services, should be bolstered. This is especially important because evidence suggests that enhancing the socioeconomic status of individuals reduces health inequities and improves health outcomes

    Embedding Based Link Prediction for Knowledge Graph Completion

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    Knowledge Graphs (KGs) are the most widely used representation of structured information about a particular domain consisting of billions of facts in the form of entities (nodes) and relations (edges) between them. Besides, the KGs also encapsulate the semantic type information of the entities. The last two decades have witnessed a constant growth of KGs in various domains such as government, scholarly data, biomedical domains, etc. KGs have been used in Machine Learning based applications such as entity linking, question answering, recommender systems, etc. Open KGs are mostly heuristically created, automatically generated from heterogeneous resources such as text, images, etc., or are human-curated. However, these KGs are often incomplete, i.e., there are missing links between the entities and missing links between the entities and their corresponding entity types. This thesis focuses on addressing these two challenges of link prediction for Knowledge Graph Completion (KGC): \textbf{(i)} General Link Prediction in KGs that include head and tail prediction, triple classification, and \textbf{(ii)} Entity Type Prediction. Most of the graph mining algorithms are proven to be of high complexity, deterring their usage in KG-based applications. In recent years, KG embeddings have been trained to represent the entities and relations in the KG in a low-dimensional vector space preserving the graph structure. In most published works such as the translational models, convolutional models, semantic matching, etc., the triple information is used to generate the latent representation of the entities and relations. In this dissertation, it is argued that contextual information about the entities obtained from the random walks, and textual entity descriptions, are the keys to improving the latent representation of the entities for KGC. The experimental results show that the knowledge obtained from the context of the entities supports the hypothesis. Several methods have been proposed for KGC and their effectiveness is shown empirically in this thesis. Firstly, a novel multi-hop attentive KG embedding model MADLINK is proposed for Link Prediction. It considers the contextual information of the entities by using random walks as well as textual entity descriptions of the entities. Secondly, a novel architecture exploiting the information contained in a pre-trained contextual Neural Language Model (NLM) is proposed for Triple Classification. Thirdly, the limitations of the current state-of-the-art (SoTA) entity type prediction models have been analysed and a novel entity typing model CAT2Type is proposed that exploits the Wikipedia Categories which is one of the most under-treated features of the KGs. This model can also be used to predict missing types of unseen entities i.e., the newly added entities in the KG. Finally, another novel architecture GRAND is proposed to predict the missing entity types in KGs using multi-label, multi-class, and hierarchical classification by leveraging different strategic graph walks in the KGs. The extensive experiments and ablation studies show that all the proposed models outperform the current SoTA models and set new baselines for KGC. The proposed models establish that the NLMs and the contextual information of the entities in the KGs together with the different neural network architectures benefit KGC. The promising results and observations open up interesting scopes for future research involving exploiting the proposed models in domain-specific KGs such as scholarly data, biomedical data, etc. Furthermore, the link prediction model can be exploited as a base model for the entity alignment task as it considers the neighbourhood information of the entities
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