Filtern
Erscheinungsjahr
Dokumenttyp
- Dissertation (833)
- Wissenschaftlicher Artikel (219)
- Buch (Monographie) (113)
- Beitrag zu einer (nichtwissenschaftlichen) Zeitung oder Zeitschrift (108)
- Arbeitspapier (62)
- Ausgabe (Heft) zu einer Zeitschrift (24)
- Teil eines Buches (Kapitel) (22)
- Konferenzveröffentlichung (17)
- Sonstiges (16)
- Rezension (10)
Sprache
- Deutsch (843)
- Englisch (518)
- Französisch (75)
- Mehrsprachig (15)
- Russisch (1)
Schlagworte
- Deutschland (84)
- Luxemburg (52)
- Stress (40)
- Schule (37)
- Schüler (33)
- Politischer Unterricht (29)
- Modellierung (28)
- Fernerkundung (25)
- Geschichte (24)
- Demokratie (23)
Institut
- Raum- und Umweltwissenschaften (213)
- Psychologie (212)
- Politikwissenschaft (132)
- Universitätsbibliothek (84)
- Rechtswissenschaft (77)
- Wirtschaftswissenschaften (66)
- Mathematik (65)
- Fachbereich 4 (57)
- Medienwissenschaft (57)
- Fachbereich 6 (50)
- Geschichte, mittlere und neuere (45)
- Fachbereich 1 (36)
- Fachbereich 3 (34)
- Informatik (31)
- Germanistik (28)
- Fachbereich 2 (26)
- Kunstgeschichte (23)
- Anglistik (21)
- Soziologie (20)
- Computerlinguistik und Digital Humanities (10)
- Philosophie (10)
- Romanistik (9)
- Fachbereich 5 (7)
- Pädagogik (6)
- Allgemeine Sprach- und Literaturwissenschaft (5)
- Ethnologie (5)
- Geschichte, alte (5)
- Klassische Philologie (4)
- Sinologie (4)
- Japanologie (3)
- Archäologie (2)
- Phonetik (2)
- Servicezentrum eSciences (2)
- Forschungszentrum Europa (1)
- Pflegewissenschaft (1)
- Slavistik (1)
- Theologische Fakultät (1)
The publication of statistical databases is subject to legal regulations, e.g. national statistical offices are only allowed to publish data if the data cannot be attributed to individuals. Achieving this privacy standard requires anonymizing the data prior to publication. However, data anonymization inevitably leads to a loss of information, which should be kept minimal. In this thesis, we analyze the anonymization method SAFE used in the German census in 2011 and we propose a novel integer programming-based anonymization method for nominal data.
In the first part of this thesis, we prove that a fundamental variant of the underlying SAFE optimization problem is NP-hard. This justifies the use of heuristic approaches for large data sets. In the second part, we propose a new anonymization method belonging to microaggregation methods, specifically designed for nominal data. This microaggregation method replaces rows in a microdata set with representative values to achieve k-anonymity, ensuring each data row is identical to at least k − 1 other rows. In addition to the overall dissimilarities of the data rows, the method accounts for errors in resulting frequency tables, which are of high interest for nominal data in practice. The method employs a typical two-step structure: initially partitioning the data set into clusters and subsequently replacing all cluster elements with representative values to achieve k-anonymity. For the partitioning step, we propose a column generation scheme followed by a heuristic to obtain an integer solution, which is based on the dual information. For the aggregation step, we present a mixed-integer problem formulation to find cluster representatives. To this end, we take errors in a subset of frequency tables into account. Furthermore, we show a reformulation of the problem to a minimum edge-weighted maximal clique problem in a multipartite graph, which allows for a different perspective on the problem. Moreover, we formulate a mixed-integer program, which combines the partitioning and the aggregation step and aims to minimize the sum of chi-squared errors in frequency tables.
Finally, an experimental study comparing the methods covered or developed in this work shows particularly strong results for the proposed method with respect to relative criteria, while SAFE shows its strength with respect to the maximum absolute error in frequency tables. We conclude that the inclusion of integer programming in the context of data anonymization is a promising direction to reduce the inevitable information loss inherent in anonymization, particularly for nominal data.
Building Fortress Europe Economic realism, China, and Europe’s investment screening mechanisms
(2023)
This thesis deals with the construction of investment screening mechanisms across the major economic powers in Europe and at the supranational level during the post-2015 period. The core puzzle at the heart of this research is how, in a traditional bastion of economic liberalism such as Europe, could a protectionist tool such as investment screening be erected in such a rapid manner. Within a few years, Europe went from a position of being highly welcoming towards foreign investment to increasingly implementing controls on it, with the focus on China. How are we to understand this shift in Europe? I posit that Europe’s increasingly protectionist shift on inward investment can be fruitfully understood using an economic realist approach, where the introduction of investment screening can be seen as part of a process of ‘balancing’ China’s economic rise and reasserting European competitiveness. China has moved from being the ‘workshop of the world’ to becoming an innovation-driven economy at the global technological frontier. As China has become more competitive, Europe, still a global economic leader, broadly situated at the technological frontier, has begun to sense a threat to its position, especially in the context of the fourth industrial revolution. A ‘balancing’ process has been set in motion, in which Europe seeks to halt and even reverse the narrowing competitiveness gap between it and China. The introduction of investment screening measures is part of this process.
In Luxemburg helfen externe Schulmediator*innen bei schulischen Konflikten. Die Anlaufstelle unterstützt bei drohenden Schulabbrüchen und Konflikten, die bei der Inklusion und Integration von Schüler*innen mit besonderem Förderbedarf oder mit Migrationshintergrund entstehen. Michèle Schilt sprach mit der Leiterin der Servicestelle, Lis De Pina, über die Arbeit der Schulmediation.
Emotionen gelten als Spiegelbild unserer persönlichen Bedürfnislage. Insbesondere in Konflikt- oder Mediationsgesprächen ist es demnach wichtig, nicht nur über den Moment zu sprechen, an dem ein Streit entstanden ist, sondern auch Bedürfnisse und Gefühle aufzudecken, die unser Handeln, Denken und Fühlen beeinflusst haben. Die folgenden Materialien zeigen, wie man als Lehrkraft Emotionen und Streit mit Grundschulkindern behandeln kann.
Sie haben eine spannende politische Diskussion in der Klasse. Das Gros Ihrer Schüler*innen ist wach, interessiert und engagiert. Alles läuft prima. Doch dann passiert's: Einer oder eine von ihnen stellt – absichtlich oder unreflektiert – eine extremistische oder verschwörungstheoretische Aussage in den Raum. Und nun?
Die Praxishefte Demokratische Schulkultur erscheinen halbjährlich und bieten Schulleitungen und Schulpersonal theoretische Grundlagen und praxisorientierte Anleitungen zur demokratiepädagogischen Schulentwicklung. Jedes Themenheft ist jeweils einer demokratiepädagogischen Bauform oder strategischen Frage der Schulentwicklung gewidmet. Die Praxishefte werden allen Luxemburger Schulen als Printausgabe zur Verfügung gestellt und online mit zusätzlichen Materialien und in französischer Fassung vorgehalten.
While humans find it easy to process visual information from the real world, machines struggle with this task due to the unstructured and complex nature of the information. Computer vision (CV) is the approach of artificial intelligence that attempts to automatically analyze, interpret, and extract such information. Recent CV approaches mainly use deep learning (DL) due to its very high accuracy. DL extracts useful features from unstructured images in a training dataset to use them for specific real-world tasks. However, DL requires a large number of parameters, computational power, and meaningful training data, which can be noisy, sparse, and incomplete for specific domains. Furthermore, DL tends to learn correlations from the training data that do not occur in reality, making DNNs poorly generalizable and error-prone.
Therefore, the field of visual transfer learning is seeking methods that are less dependent on training data and are thus more applicable in the constantly changing world. One idea is to enrich DL with prior knowledge. Knowledge graphs (KG) serve as a powerful tool for this purpose because they can formalize and organize prior knowledge based on an underlying ontological schema. They contain symbolic operations such as logic, rules, and reasoning, and can be created, adapted, and interpreted by domain experts. Due to the abstraction potential of symbols, KGs provide good prerequisites for generalizing their knowledge. To take advantage of the generalization properties of KG and the ability of DL to learn from large-scale unstructured data, attempts have long been made to combine explicit graph and implicit vector representations. However, with the recent development of knowledge graph embedding methods, where a graph is transferred into a vector space, new perspectives for a combination in vector space are opening up.
In this work, we attempt to combine prior knowledge from a KG with DL to improve visual transfer learning using the following steps: First, we explore the potential benefits of using prior knowledge encoded in a KG for DL-based visual transfer learning. Second, we investigate approaches that already combine KG and DL and create a categorization based on their general idea of knowledge integration. Third, we propose a novel method for the specific category of using the knowledge graph as a trainer, where a DNN is trained to adapt to a representation given by prior knowledge of a KG. Fourth, we extend the proposed method by extracting relevant context in the form of a subgraph of the KG to investigate the relationship between prior knowledge and performance on a specific CV task. In summary, this work provides deep insights into the combination of KG and DL, with the goal of making DL approaches more generalizable, more efficient, and more interpretable through prior knowledge.