January 2025 - informatik-vortraege

Einladung Informatik Oberseminar Lubna Ali
by Bombelka, Angelika 28 Feb '25

28 Feb '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 30. Januar 2024, 14.00 Uhr Ort: 9222, E3, Ahornstr. 55 und hybrid via Zoom (https://rwth.zoom-x.de/j/64937773189?pwd=eGttNUMzSElnQUVkc3FrYzBqK2F4UT09) Referent: Lubna Ali M.Sc. RWTH Lehr- und Forschungsgebiet Informatik 9 (Lerntechnologien) Thema: convOERter: A Technical Assistance Tool to Support Semi-Automatic Conversion of Images in Educational Materials as OER Abstract: Open Educational Resources (OER) are seen as an important element in the process of digitizing higher education teaching and as essential building blocks for openness in education. They can be defined as teaching, learning, and research materials that have been made openly available, shareable, and modifiable. OER include different types of resources such as full courses, textbooks, videos, presentations, tests, and images, which are usually published under the open Creative Commons licences. OER can play an important role in improving education by facilitating access to high quality digital educational materials. Accordingly, there is a steady increase among higher education institutions to participate in the so-called "open movement" in general and in utilizing OER in particular. Nevertheless, there are many challenges that still face the deployment of OER in the educational context. One of the main challenges is the production of new OER materials and converting already existing materials into OER, which could be viable by qualifying educators through training courses and/or supporting them with specific tools. There are many platforms and tools that support the creation of new OER content. However, to our knowledge, there are no tools that perform fully- or semi-automatic conversion of already existing educational materials. This identified gap was the basis for the design and implementation of the OER conversion tool (convOERter). The tool supports the user by semi-automatically converting educational materials containing images into OER-compliant materials. The main functionality of the tool is based on reading a file, extracting all images as well as all possible metadata, and substituting the extracted images with OER elements in a semi-automated way. The retrieved OER images are referenced and licenced properly according to the known TASLL rule. Finally, the entire file is automatically licenced under Creative Commons excluding specific elements from the entire licence such as logos. In order to evaluate the effectiveness of the tool in promoting the use of OER, a comprehensive user study was conducted with educators and OER enthusiastic at different universities. The study was accomplished by offering a series of OER evaluation workshops to compare the conversion efficiency of the tool with manual conversion. The results show that using the conversion tool improves the conversion process in terms of speed, license quality, and total efficiency. These results highlight that the tool can be a valuable addition to the community, especially for users less experienced with OER. As a future work, it is intended to further develop the tool and improve its functionality. Additionally, a long-term study can be conducted to assess the impact of the tool in facilitating and enhancing the production of OER on a larger scale. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Simon Schwitanski
by Simon Schwitanski 21 Jan '25

21 Jan '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 5. Februar 2025, 13.00 Uhr Ort: Seminarraum 003, IT Center, Kopernikusstraße 6 Referent: Simon Schwitanski, M.Sc. Lehrstuhl für Hochleistungsrechnen (Informatik 12) Thema: Modeling Synchronization and Consistency for Data Race Detection in Remote Memory Access Programs Abstract: The increasing parallelism in today's supercomputers requires scalable parallel programming methods with efficient communication models. The traditional communication model in scientific computing is message passing: Both the sending and receiving process participate actively in the data exchange via messages. Remote Memory Access (RMA) models provide an alternative communication method where processes can access the memory of other processes directly. RMA models avoid unnecessary synchronization between processes and outperform the traditional message-passing model in modern supercomputers. However, they require users to explicitly ensure the synchronization and consistency of memory accesses through corresponding API calls. Otherwise, concurrent conflicting memory accesses lead to data races with undefined behavior. The non-deterministic nature of data races, commonly known from shared-memory programming, makes them difficult to detect manually. This thesis investigates data races in RMA programs and provides novel scalable methods to detect them at runtime. A classification of data races in the RMA models MPI~RMA, OpenSHMEM, and GASPI shows that synchronization and consistency are the two key properties that a correctness tool must capture to identify RMA races. This thesis provides formal models that allow analyzing both properties in RMA programs. The synchronization model analyzes the happened-before relation of events using a vector clock exchange. It captures the synchronization state of processes at runtime. The consistency model formalizes a relation defining when a remote memory access is guaranteed to be completed. Both models are combined in a generalized on-the-fly race detection model that can detect RMA data races independent of the concrete RMA model used in an application. The developed race detection model is implemented in a tool named RMASanitizer. It combines the shared-memory race detector ThreadSanitizer with the correctness checking tool MUST to detect RMA data races in MPI RMA, OpenSHMEM, and GASPI at runtime. For the evaluation, this thesis provides RMARaceBench, a classification quality benchmark suite designed to quantify the detection accuracy of RMA race detection tools. The evaluation with RMARaceBench shows that RMASanitizer has the highest detection accuracy compared to other state-of-the-art RMA race detectors. An overhead study with RMA proxy applications running with up to 700 processes shows that RMASanitizer is applicable to large-scale workloads. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Herr Sascha Martin Welten
by Maaßen, Leany 06 Jan '25

06 Jan '25

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 17.01.2025, 14:00-15:00 Uhr Ort: Raum 5053.2 (großer B-IT-Hörsaal)/Informatikzentrum, Ahornstraße 55 Referent: Herr Sascha Martin Welten, M. Sc. Lehrstuhl Informatik 5 Thema: Methods for Practical Data Sharing and Decentralised Analytics: An Integrated Approach Abstract: This dissertation addresses the challenges of sharing data across organisational boundaries, which is crucial for scientific progress, particularly with the rise of AI and data-driven approaches. While data sharing is essential for innovation, concerns around privacy, security, and regulatory compliance often limit access to valuable data. This work introduces an integrated framework that combines privacy-preserving methods with Decentralised Analytics (DA), addressing key requirements such as trust, controlled access, and data heterogeneity. The research develops a DevOps-inspired lifecycle, 'DAOps', for managing shared data analysis, implemented in a novel platform, PADME (Platform for Analytics and Distributed Machine Learning for Enterprises). PADME supports various types of analysis and enables decentralised model training, offering a privacy-preserving alternative to traditional centralised methods. The platforms evaluation in healthcare and hydrology confirms its operational readiness and applicability to real-world research scenarios. Key findings highlight the importance of common data schema standards and balanced data distributions for successful data sharing. Ultimately, this dissertation contributes to the practical realisation of data-sharing concepts, promoting secure, decentralised data analysis and fostering data-driven research collaborations. Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________ Leany Maaßen RWTH Aachen University Lehrstuhl Informatik 5, LuFG Informatik 5 Prof. Dr. Stefan Decker, Prof. Gerhard Lakemeyer Ph.D., JunProf. Dr. Sandra Geisler Ahornstrasse 55 D-52074 Aachen Tel: 0241-80-21509 Fax: 0241-80-22321 E-Mail: maassen(a)dbis.rwth-aachen.de

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