687 research outputs found

    Configuration Management of Distributed Systems over Unreliable and Hostile Networks

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    Economic incentives of large criminal profits and the threat of legal consequences have pushed criminals to continuously improve their malware, especially command and control channels. This thesis applied concepts from successful malware command and control to explore the survivability and resilience of benign configuration management systems. This work expands on existing stage models of malware life cycle to contribute a new model for identifying malware concepts applicable to benign configuration management. The Hidden Master architecture is a contribution to master-agent network communication. In the Hidden Master architecture, communication between master and agent is asynchronous and can operate trough intermediate nodes. This protects the master secret key, which gives full control of all computers participating in configuration management. Multiple improvements to idempotent configuration were proposed, including the definition of the minimal base resource dependency model, simplified resource revalidation and the use of imperative general purpose language for defining idempotent configuration. Following the constructive research approach, the improvements to configuration management were designed into two prototypes. This allowed validation in laboratory testing, in two case studies and in expert interviews. In laboratory testing, the Hidden Master prototype was more resilient than leading configuration management tools in high load and low memory conditions, and against packet loss and corruption. Only the research prototype was adaptable to a network without stable topology due to the asynchronous nature of the Hidden Master architecture. The main case study used the research prototype in a complex environment to deploy a multi-room, authenticated audiovisual system for a client of an organization deploying the configuration. The case studies indicated that imperative general purpose language can be used for idempotent configuration in real life, for defining new configurations in unexpected situations using the base resources, and abstracting those using standard language features; and that such a system seems easy to learn. Potential business benefits were identified and evaluated using individual semistructured expert interviews. Respondents agreed that the models and the Hidden Master architecture could reduce costs and risks, improve developer productivity and allow faster time-to-market. Protection of master secret keys and the reduced need for incident response were seen as key drivers for improved security. Low-cost geographic scaling and leveraging file serving capabilities of commodity servers were seen to improve scaling and resiliency. Respondents identified jurisdictional legal limitations to encryption and requirements for cloud operator auditing as factors potentially limiting the full use of some concepts

    Microcredentials to support PBL

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    Method versatility in analysing human attitudes towards technology

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    Various research domains are facing new challenges brought about by growing volumes of data. To make optimal use of them, and to increase the reproducibility of research findings, method versatility is required. Method versatility is the ability to flexibly apply widely varying data analytic methods depending on the study goal and the dataset characteristics. Method versatility is an essential characteristic of data science, but in other areas of research, such as educational science or psychology, its importance is yet to be fully accepted. Versatile methods can enrich the repertoire of specialists who validate psychometric instruments, conduct data analysis of large-scale educational surveys, and communicate their findings to the academic community, which corresponds to three stages of the research cycle: measurement, research per se, and communication. In this thesis, studies related to these stages have a common theme of human attitudes towards technology, as this topic becomes vitally important in our age of ever-increasing digitization. The thesis is based on four studies, in which method versatility is introduced in four different ways: the consecutive use of methods, the toolbox choice, the simultaneous use, and the range extension. In the first study, different methods of psychometric analysis are used consecutively to reassess psychometric properties of a recently developed scale measuring affinity for technology interaction. In the second, the random forest algorithm and hierarchical linear modeling, as tools from machine learning and statistical toolboxes, are applied to data analysis of a large-scale educational survey related to students’ attitudes to information and communication technology. In the third, the challenge of selecting the number of clusters in model-based clustering is addressed by the simultaneous use of model fit, cluster separation, and the stability of partition criteria, so that generalizable separable clusters can be selected in the data related to teachers’ attitudes towards technology. The fourth reports the development and evaluation of a scholarly knowledge graph-powered dashboard aimed at extending the range of scholarly communication means. The findings of the thesis can be helpful for increasing method versatility in various research areas. They can also facilitate methodological advancement of academic training in data analysis and aid further development of scholarly communication in accordance with open science principles.Verschiedene Forschungsbereiche müssen sich durch steigende Datenmengen neuen Herausforderungen stellen. Der Umgang damit erfordert – auch in Hinblick auf die Reproduzierbarkeit von Forschungsergebnissen – Methodenvielfalt. Methodenvielfalt ist die Fähigkeit umfangreiche Analysemethoden unter Berücksichtigung von angestrebten Studienzielen und gegebenen Eigenschaften der Datensätze flexible anzuwenden. Methodenvielfalt ist ein essentieller Bestandteil der Datenwissenschaft, der aber in seinem Umfang in verschiedenen Forschungsbereichen wie z. B. den Bildungswissenschaften oder der Psychologie noch nicht erfasst wird. Methodenvielfalt erweitert die Fachkenntnisse von Wissenschaftlern, die psychometrische Instrumente validieren, Datenanalysen von groß angelegten Umfragen im Bildungsbereich durchführen und ihre Ergebnisse im akademischen Kontext präsentieren. Das entspricht den drei Phasen eines Forschungszyklus: Messung, Forschung per se und Kommunikation. In dieser Doktorarbeit werden Studien, die sich auf diese Phasen konzentrieren, durch das gemeinsame Thema der Einstellung zu Technologien verbunden. Dieses Thema ist im Zeitalter zunehmender Digitalisierung von entscheidender Bedeutung. Die Doktorarbeit basiert auf vier Studien, die Methodenvielfalt auf vier verschiedenen Arten vorstellt: die konsekutive Anwendung von Methoden, die Toolbox-Auswahl, die simultane Anwendung von Methoden sowie die Erweiterung der Bandbreite. In der ersten Studie werden verschiedene psychometrische Analysemethoden konsekutiv angewandt, um die psychometrischen Eigenschaften einer entwickelten Skala zur Messung der Affinität von Interaktion mit Technologien zu überprüfen. In der zweiten Studie werden der Random-Forest-Algorithmus und die hierarchische lineare Modellierung als Methoden des Machine Learnings und der Statistik zur Datenanalyse einer groß angelegten Umfrage über die Einstellung von Schülern zur Informations- und Kommunikationstechnologie herangezogen. In der dritten Studie wird die Auswahl der Anzahl von Clustern im modellbasierten Clustering bei gleichzeitiger Verwendung von Kriterien für die Modellanpassung, der Clustertrennung und der Stabilität beleuchtet, so dass generalisierbare trennbare Cluster in den Daten zu den Einstellungen von Lehrern zu Technologien ausgewählt werden können. Die vierte Studie berichtet über die Entwicklung und Evaluierung eines wissenschaftlichen wissensgraphbasierten Dashboards, das die Bandbreite wissenschaftlicher Kommunikationsmittel erweitert. Die Ergebnisse der Doktorarbeit tragen dazu bei, die Anwendung von vielfältigen Methoden in verschiedenen Forschungsbereichen zu erhöhen. Außerdem fördern sie die methodische Ausbildung in der Datenanalyse und unterstützen die Weiterentwicklung der wissenschaftlichen Kommunikation im Rahmen von Open Science

    A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

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    The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

    Translating Islamic Law: the postcolonial quest for minority representation

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    This research sets out to investigate how culture-specific or signature concepts are rendered in English-language discourse on Islamic, or ‘shariʿa’ law, which has Arabic roots. A large body of literature has investigated Islamic law from a technical perspective. However, from the perspective of linguistics and translation studies, little attention has been paid to the lexicon that makes up this specialised discourse. Much of the commentary has so far been prescriptive, with limited empirical evidence. This thesis aims to bridge this gap by exploring how ‘culturalese’ (i.e., ostensive cultural discourse) travels through language, as evidenced in the self-built Islamic Law Corpus (ILC), a 9-million-word monolingual English corpus, covering diverse genres on Islamic finance and family law. Using a mixed methods design, the study first quantifies the different linguistic strategies used to render shariʿa-based concepts in English, in order to explore ‘translation’ norms based on linguistic frequency in the corpus. This quantitative analysis employs two models: profile-based correspondence analysis, which considers the probability of lexical variation in expressing a conceptual category, and logistic regression (using MATLAB programming software), which measures the influence of the explanatory variables ‘genre’, ‘legal function’ and ‘subject field’ on the choice between an Arabic loanword and an endogenous English lexeme, i.e., a close English equivalent. The findings are then interpreted qualitatively in the light of postcolonial translation agendas, which aim to preserve intangible cultural heritage and promote the representation of minoritised groups. The research finds that the English-language discourse on Islamic law is characterised by linguistic borrowing and glossing, implying an ideologically driven variety of English that can be usefully labelled as a kind of ‘Islamgish’ (blending ‘Islamic’ and ‘English’) aimed at retaining symbols of linguistic hybridity. The regression analysis confirms the influence of the above-mentioned contextual factors on the use of an Arabic loanword versus English alternatives
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