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Soziale Netzwerke spielen im Alltagsleben der Schülerinnen und Schüler eine entscheidende Rolle. Im Rahmen der vorliegenden Masterarbeit wurde ein Konzept für die Anzeige von Profilvorschlägen innerhalb des sozialen Netzwerks „InstaHub“, welches ein speziell für den Informatikunterricht programmiertes Werkzeug zum Thema „Datenbanken“ darstellt, entwickelt. Als Hürde stellte sich dabei dar, dass von den etablierten sozialen Netzwerken nur wenig bis gar keine Informationen über die Berechnung von Profil- oder Freundschaftsvorschlägen preisgegeben werden. Daher wurde zunächst das Wesen von Beziehungen zwischen Menschen in nicht-internetbasierten und in internetbasierten sozialen Netzwerken sowie die Gründe für Beziehungen zwischen Menschen in diesen Netzwerken dargelegt. Anhand der Beobachtung von Vorschlägen in anderen sozialen Netzwerken sowie der in InstaHub gespeicherten Nutzerdaten wurde ein Algorithmus für Profilvorschläge in InstaHub entworfen und mitsamt einer passenden Visualisierung entsprechend implementiert. Den zweiten Teil der Arbeit bildete eine Unterrichtseinheit für die Sekundarstufe II mit dem Thema Gefahren der Erzeugung und Verarbeitung von personenbezogenen Daten. In der Unterrichtseinheit dienen die Profilvorschläge in InstaHub, die auf von InstaHub über dessen Nutzer gesammelten Daten aufbauen, als Einstieg in die Thematik. Anschließend wird der Fokus von sozialen Netzwerken auf andere Online-Dienste erweitert und auf die Verarbeitung und Weitergabe dieser Daten eingegangen.
This thesis focuses on approximate inference in assumption-based argumentation frameworks. Argumentation provides a significant idea in the computerization of theoretical and practical reasoning in AI. And it has a close connection with AI, engaging in arguments to perform scientific reasoning. The fundamental approach in this field is abstract argumentation frameworks developed by Dung. Assumption-based argumentation can be regarded as an instance of abstract argumentation with structured arguments. When facing a large scale of data, a challenge of reasoning in assumption-based argumentation is how to construct arguments and resolve attacks over a given claim with minimal cost of computation and acceptable accuracy at the same time. This thesis proposes and investigates approximate methods that randomly select and construct samples of frameworks based on graphical dispute derivations to solve this problem. The presented approach aims to improve reasoning performance and get an acceptable trade-off between computational time and accuracy. The evaluation shows that for reasoning in assumption-based argumentation, in general, the running time is reduced with the cost of slightly low accuracy by randomly sampling and constructing inference rules for potential arguments over a query.
The Material Point Method (MPM) has proven to be a very capable simulation method in computer graphics that is able to model materials that were previously very challenging to animate [1, 2]. Apart from simulating singular materials, the simulation of multiple materials that interact with each other introduces new challenges. This is the focus of this thesis. It will be shown that the self-collision capabilities of the MPM can naturally handle multiple materials interacting in the same scene on a collision basis, even if the materials use distinct constitutive models. This is then extended by porous interaction of materials as in[3], which also integrates easily with MPM.It will furthermore be shown that regular single-grid MPM can be viewed as a subset of this multi-grid approach, meaning that its behavior can also be achieved if multiple grids are used. The porous interaction is generalized to arbitrary materials and freely changeable material interaction terms, yielding a flexible, user-controllable framework that is independent of specific constitutive models. The framework is implemented on the GPU in a straightforward and simple way and takes advantage of the rasterization pipeline to resolve write-conflicts, resulting in a portable implementation with wide hardware support, unlike other approaches such as [4].
„La liaison est un phénomène complexe dont la phénoménologie est encore aujourd’hui sujette à recherches et à débats. Dans la littérature classique, orthoépique ou descriptive, comme dans les recherches les plus actuelles, la liaison est considérée comme un phénomène multi-paramétrique et tous les niveaux linguistiques sont convoqués : phonologie, prosodie et syllabation, morphologie, syntaxe, lexique et sémantique, diachronie, orthographe et différentiation des styles [...] toutes les dimensions de la variation externe : variation dans le temps, dans l’espace géographique et dans l’espace social, variation dans l’espace stylistique des genres de discours“
(Eychenne/Laks 2017:1).
Dieses Zitat beschreibt die Liaison als ein sehr komplexes, von vielen Parametern beeinflusstes Phänomen. Wie gehen Lernende 1 mit einem solchen Phänomen um? Welche Liaison realisie-ren sie wie häufig? Welche Fehler treten auf? Welche Gründe gibt es für diese Fehler? Welche Auswirkungen hat ein längerer Auslandsaufenthalt des Lernenden in einem französischsprachi-gen Land auf die Produktion von Liaisons? Gibt es Unterschiede zwischen dem Erwerb der Liaison bei Kindern mit Französisch als Erstsprache (L1) und Lernenden des Französischen als Fremdsprache (L2)?
Auf all diese Fragen möchte ich im Laufe der vorliegenden Arbeit eingehen. Nach dem Zusam-mentragen einiger grundlegender Fakten über die Liaison soll daher ein Korpus mit französi-schen Sprachaufnahmen von deutschen Studierenden ausgewertet werden. Die Ergebnisse wer-den im Anschluss präsentiert und zunächst mit Resultaten von Kindern mit Französisch als L1 sowie anschließend mit Ergebnissen anderer Studien über Französischlernende verglichen.
The industry standard Decision Model and Notation (DMN) has enabled a new way for the formalization of business rules since 2015. Here, rules are modeled in so-called decision tables, which are defined by input columns and output columns. Furthermore, decisions are arranged in a graph-like structure (DRD level), which creates dependencies between them. With a given input, the decisions now can be requested by appropriate systems. Thereby, activated rules produce output for future use. However, modeling mistakes produces erroneous models, which can occur in the decision tables as well as at the DRD level. According to the Design Science Research Methodology, this thesis introduces an implementation of a verification prototype for the detection and resolution of these errors while the modeling phase. Therefore, presented basics provide the needed theoretical foundation for the development of the tool. This thesis further presents the architecture of the tool and the implemented verification capabilities. Finally, the created prototype is evaluated.
On-screen interactive presentations have got immense popularity in the domain of attentive interfaces recently. These attentive screens adapt their behavior according to the user's visual attention. This thesis aims to introduce an application that would enable these attentive interfaces to change their behavior not just according to the gaze data but also facial features and expressions. The modern era requires new ways of communications and publications for advertisement. These ads need to be more specific according to people's interests, age, and gender. When advertising, it's important to get a reaction from the user but not every user is interested in providing feedback. In such a context more, advance techniques are required that would collect user's feedback effortlessly. The main problem this thesis intends to resolve is, to apply advanced techniques of gaze and face recognition to collect data about user's reactions towards different ads being played on interactive screens. We aim to create an application that enables attentive screens to detect a person's facial features, expressions, and eye gaze. With eye gaze data we can determine the interests and with facial features, age and gender can be specified. All this information will help in optimizing the advertisements.
Blockchain in Healthcare
(2020)
The underlying characteristics of blockchain can facilitate data provenance, data integrity, data security, and data management. It has the potential to transform the healthcare sector. Since the introduction of Bitcoin in the fintech industry, the blcockhain technology has been gaining a lot of traction and its purpose is not just limited to finance. This thesis highlights the inner workings of blockchain technology and its application areas with possible existing solutions. Blockchain could lay the path for a new revolution in conventional healthcare systems. We presented how individual sectors within the healthcare industry could use blockchain and what solution persists. Also, we have presented our own concept to improve the existing paper-based prescription management system which is based on Hyperledger framework. The results of this work suggest that healthcare can benefit from blockchain technology bringing in the new ways patients can be treated.
Since the invention of U-net architecture in 2015, convolutional networks based on its encoder-decoder approach significantly improved results in image analysis challenges. It has been proven that such architectures can also be successfully applied in different domains by winning numerous championships in recent years. Also, the transfer learning technique created an opportunity to push state-of-the-art benchmarks to a higher level. Using this approach is beneficial for the medical domain, as collecting datasets is generally a difficult and expensive process.
In this thesis, we address the task of semantic segmentation with Deep Learning and make three main contributions and release experimental results that have practical value for medical imaging.
First, we evaluate the performance of four neural network architectures on the dataset of the cervical spine MRI scans. Second, we use transfer learning from models trained on the Imagenet dataset and compare it to randomly initialized networks. Third, we evaluate models trained on the bias field corrected and raw MRI data. All code to reproduce results is publicly available online.
Constituent parsing attempts to extract syntactic structure from a sentence. These parsing systems are helpful in many NLP applications such as grammar checking, question answering, and information extraction. This thesis work is about implementing a constituent parser for German language using neural networks. Over the past, recurrent neural networks have been used in building a parser and also many NLP applications. In this, self-attention neural network modules are used intensively to understand sentences effectively. With multilayered self-attention networks, constituent parsing achieves 93.68% F1 score. This is improved even further by using both character and word embeddings as a representation of the input. An F1 score of 94.10% was the best achieved by constituent parser using only the dataset provided. With the help of external datasets such as German Wikipedia, pre-trained ELMo models are used along with self-attention networks achieving 95.87% F1 score.
Thesis is devoted to the topic of challenges and solutions for human resources management (HRM) in international organizations. The aim is to investigate methodological approaches to assessment of HRM challenges and solutions, and to apply them on practice, to develop ways of improvement of HRM of a particular enterprise. The practical research question investigated is “Is the Ongoing Professional Development – Strategic HRM (OPD-SHRM) model a better solution for HRM system of PrJSC “Philip Morris Ukraine”?”
To achieve the aim of this work and to answer the research question, we have studied theoretical approaches to explaining and assessing HRM in section 1, analyzed HRM system of an international enterprise in section 2, and then synthesized theory and practice to find intersection points in section 3.
Research findings indicate that the main challenge of HRM is to balance between individual and organizational interests. Implementation of OPD-SHRM is one of the solutions. Switching focus from satisfaction towards success will bring both tangible and intangible benefits for individuals and organization. In case of PrJSC “Philip Morris Ukraine”, the maximum forecasted increase is 330% in net profit, 350% in labor productivity, and 26% in Employee Development and Engagement Index.
