9,614 research outputs found

    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

    Comparative Multiple Case Study into the Teaching of Problem-Solving Competence in Lebanese Middle Schools

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    This multiple case study investigates how problem-solving competence is integrated into teaching practices in private schools in Lebanon. Its purpose is to compare instructional approaches to problem-solving across three different programs: the American (Common Core State Standards and New Generation Science Standards), French (Socle Commun de Connaissances, de Compétences et de Culture), and Lebanese with a focus on middle school (grades 7, 8, and 9). The project was conducted in nine schools equally distributed among three categories based on the programs they offered: category 1 schools offered the Lebanese program, category 2 the French and Lebanese programs, and category 3 the American and Lebanese programs. Each school was treated as a separate case. Structured observation data were collected using observation logs that focused on lesson objectives and specific cognitive problem-solving processes. The two logs were created based on a document review of the requirements for the three programs. Structured observations were followed by semi-structured interviews that were conducted to explore teachers' beliefs and understandings of problem-solving competence. The comparative analysis of within-category structured observations revealed an instruction ranging from teacher-led practices, particularly in category 1 schools, to more student-centered approaches in categories 2 and 3. The cross-category analysis showed a reliance on cognitive processes primarily promoting exploration, understanding, and demonstrating understanding, with less emphasis on planning and executing, monitoring and reflecting, thus uncovering a weakness in addressing these processes. The findings of the post-observation semi-structured interviews disclosed a range of definitions of problem-solving competence prevalent amongst teachers with clear divergences across the three school categories. This research is unique in that it compares problem-solving teaching approaches across three different programs and explores underlying teachers' beliefs and understandings of problem-solving competence in the Lebanese context. It is hoped that this project will inform curriculum developers about future directions and much-anticipated reforms of the Lebanese program and practitioners about areas that need to be addressed to further improve the teaching of problem-solving competence

    An Exploration of the Suitability of Pharmacy Education in Saudi Arabia to Prepare Graduates to Meet Healthcare Needs: a Mixed-Methods Study

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    The key role of pharmacists within the health system, particularly in optimising safe, responsible and effective use of medicines, underpins the demand for a highly skilled and competent workforce. Therefore, developing the capacity of pharmacists to attain and maintain essential competencies relevant to the population’s health needs is required to ensure a high standard of patient care, thereby helping to improve patient and population health. In Saudi Arabia, little evidence exists regarding the assessment of national educational programmes’ structure and outcomes. Moreover, no national competency framework exists for pharmacists in any sector or stage of practice. In the absence of such core quality elements to inform pharmacy education assessment and development, the extent to which pharmacy schools in Saudi Arabia prepare competent pharmacists to address societal needs from pharmacy services is unclear. Therefore, this study aimed to explore the extent to which pharmacy education can prepare competent pharmacists to address the healthcare needs for pharmacy practice in Saudi Arabia. An exploratory sequential mixed methods research design was used to address the aim of this study in three phases: individual interviews and focus groups were employed with a purposively selected sample of pharmacy policy makers, pharmacists and the public to explore societal healthcare needs and the roles required of pharmacists to meet those needs; a national online survey of pharmacists and an online nominal group consensus method of pharmacy experts were used to identify competencies considered essential to develop a profession-wide national foundation level competency framework; and a case study in which curriculum mapping of two purposively selected Doctor of Pharmacy (PharmD) curricula was used to assess the extent to which the current pharmacy programme in Saudi Arabia meets the identified competencies of the developed national competency framework. Based on qualitative and quantitative analyses of societal healthcare needs, pharmacists’ roles, core competencies and curricular contents within the local context of Saudi Arabia, findings showed that there is a mismatch between initial education and real practice needs and expectations. While the country’s current needs from pharmacists are to optimise health system capacity and increase access to primary care services and medicines expertise in community pharmacies, the study indicated local education is product-oriented with a focus of curricular content and experiential training opportunities in most schools on preparing future pharmacists for hospital pharmacy practice. The study also identified several gaps between current initial education programmes and the competencies required to practise the expected roles, suggesting that current initial education might not prepare the students sufficiently to provide the full range of quality pharmaceutical services as per the country’s pharmacy practice needs. The study provided a new understanding of graduates’ readiness to practise as per the country’s pharmacy practice needs, the quality of educational programmes and pharmacists' professional development opportunities in Saudi Arabia. Findings maybe used to inform the development of competency-based education and maximise graduates’ capacity to deliver and develop pharmaceutical services effectively to best meet societal healthcare needs in Saudi Arabia

    An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains

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    This research aimed to develop an empirical understanding of the relationships between integration, dynamic capabilities and performance in the supply chain domain, based on which, two conceptual frameworks were constructed to advance the field. The core motivation for the research was that, at the stage of writing the thesis, the combined relationship between the three concepts had not yet been examined, although their interrelationships have been studied individually. To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative study, which was undertaken via multiple case studies to investigate lines of enquiry that would address the research questions formulated. This is consistent with the author’s philosophical adoption of the ontology of relativism and the epistemology of constructionism, which was considered appropriate to address the research questions. Empirical data and evidence were collected, and various triangulation techniques were employed to ensure their credibility. Some key features of grounded theory coding techniques were drawn upon for data coding and analysis, generating two levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in improving performance, the performance also informed the former. This reflects a cyclical and iterative approach rather than one purely based on linearity. Adopting a holistic approach towards the relationship was key in producing complementary strategies that can deliver sustainable supply chain performance. The research makes theoretical, methodological and practical contributions to the field of supply chain management. The theoretical contribution includes the development of two emerging conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed insight into their correlations. The latter gives a holistic view of their relationships and how they are connected, reflecting a middle-range theory that bridges theory and practice. The methodological contribution lies in presenting models that address gaps associated with the inconsistent use of terminologies in philosophical assumptions, and lack of rigor in deploying case study research methods. In terms of its practical contribution, this research offers insights that practitioners could adopt to enhance their performance. They can do so without necessarily having to forgo certain desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities

    Machine learning and mixed reality for smart aviation: applications and challenges

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    The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency

    Role of Digitalization in Election Voting Through Industry 4.0 Enabling Technologies

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    The election voting system is one of the essential pillars of democracy to elect the representative for ruling the country. In the election voting system, there are multiple areas such as detection of fake voters, illegal activities for fake voting, booth capturing, ballot monitoring, etc., in which Industry 4.0 can be adopted for the application of real-time monitoring, intelligent detection, enhancing security and transparency of voting and other data during the voting. According to previous research, there are no studies that have presented the significance of industry 4.0 technologies for improving the electronic voting system from a sustainability standpoint. To overcome the research gap, this study aims to present literature about Industry 4.0 technologies on the election voting system. We examined individual industry enabling technologies such as blockchain, artificial intelligence (AI), cloud computing, and the Internet of Things (IoT) that have the potential to strengthen the infrastructure of the election voting system. Based upon the analysis, the study has discussed and recommended suggestions for the future scope such as: IoT and cloud computing-based automatic systems for the detection of fake voters and updating voter attendance after the verification of the voter identity; AI-based illegal, and fake voting activities detection through vision node; blockchain-inspired system for the data integrity in between voter and election commission and robotic assistance system for guiding the voter and also for detecting disputes in the premises of election booth

    DEVELOPMENT OF PROBLEM-SPECIFIC MODELING LANGUAGE TO SUPPORT SOFTWARE VARIABILITY IN "SMART HOME" SYSTEMS

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    Building conceptual models for software design, in particular for high-tech applications such as smart home systems, is a complex task that significantly affects the efficiency of their development processes. One of the innovative methods of solving this problem is the use of domain-specific modeling languages (DSMLs), which can reduce the time and other project resources required to create such systems. The subject of research in this paper is approaches to the development of DSML for Smart Home systems as a separate class of Internet of Things systems. The purpose of this work is to propose an approach to the development of DSMLs based on a model of variability of the properties of such a system. The following tasks are being solved: analysis of some existing approaches to the creation of DSMLs; construction of a multifaceted classification of requirements for them, application of these requirements to the design of the syntax of a specific DSML-V for the creation of variable software in smart home systems; development of a technological scheme and quantitative metrics for experimental evaluation of the effectiveness of the proposed approach. The following methods are used: variability modeling based on the property model, formal notations for describing the syntax of the DSML-V language, and the use of the open CASE tool metaDepth. Results: a multifaceted classification of requirements for a broad class of DSML languages is built; the basic syntactic constructions of the DSML-V language are developed to support the properties of software variability of "Smart Home" systems; a formal description of such syntax in the Backus-Naur notation is given; a technological scheme for compiling DSML-V specifications into the syntax of the language of the open CASE tool metaDepth is created; the effectiveness of the proposed approach using quantitative metrics is experimentally investigated. Conclusions: the proposed method of developing a specialized problem-oriented language for smart home systems allows for multilevel modeling of the variability properties of its software components and provides an increase in the efficiency of programming such models by about 14% compared to existing approaches

    Bildung in der digitalen Transformation

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    Die Coronapandemie und der durch sie erzwungene zeitweise Übergang von Präsenz- zu Distanzlehre haben die Digitalisierung des Bildungswesens enorm vorangetrieben. Noch deutlicher als vorher traten dabei positive wie negative Aspekte dieser Entwicklung zum Vorschein. Während den Hochschulen der Wechsel mit vergleichsweise geringen Reibungsverlusten gelang, offenbarten sich diese an Schulen weitaus deutlicher. Trotz aller Widrigkeiten erscheint eines klar: Die zeitweisen Veränderungen werden Nachwirkungen zeigen. Eine völlige Rückkehr zum Status quo ante ist kaum noch vorstellbar. Zwei Fragen bestimmen vor diesem Hintergrund die Doppelgesichtigkeit des Themas der 29. Jahrestagung der Gesellschaft für Medien in der Wissenschaft (GMW). Erstens: Wie ‚funktioniert‘ Bildung in der sich derzeit ereignenden digitalen Transformation und welche Herausforderungen gibt es? Und zweitens: Befindet sich möglicherweise Bildung selbst in der Transformation? Beiträge zu diesen und weiteren Fragen vereint der vorliegende Tagungsband

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution
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