informatik-vortraege Februar 2024

informatik-vortraege@lists.rwth-aachen.de

3 Teilnehmer
3 Diskussionen

Einladung Informatik Oberseminar Lubna Ali

von Bombelka, Angelika

+********************************************************************** * * * 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

3 Wochen, 1 Tag

Einladung: Informatik-Oberseminar Ali Athar

von Ali Athar

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 5. März 2024, 10.00 Uhr Ort: Raum 025, Mies-van-der-Rohe Str. 15 (UMIC Gebäude) Referent: Ali Athar, M.Sc. Lehrstuhl Informatik 13 Thema: Segmenting and Tracking Objects in Video Abstract: Research to develop methods that can accurately localize and track objects in video has been ongoing for decades. Approaches capable of accomplishing this are highly sought after for a variety of applications including autonomous robots, self-driving vehicles, sports analytics, video editing, etc. Despite significant progress in recent times, the task is far from solved, in particular for challenging scenarios involving occlusions, motion blur, and camera ego-motion. In this thesis, we present a series of works that advance the state of research in this domain in various ways, as outlined below. Our first work, STEm-Seg, is an end-to-end trainable method for instance segmentation that models the input video as a single 3D space-time volume and relies on clustering per-pixel embeddings to segment and track objects. This differs from existing approaches, which largely follow the tracking-by-detection paradigm. Our novel formulation for these embeddings enables us to cluster the embeddings in an efficient and end-to-end learned fashion. The second work, called HODOR, is aimed at mitigating the need for densely annotated data for training video tracking methods. Specifically, it tackles the task of Video Object Segmentation (VOS) in a weakly supervised manner where it can be trained using static images or sparsely annotated video. To this end, we adopt a novel approach that encodes objects into concise descriptors. This is in contrast to existing approaches that predominantly learn space-time correspondences, which makes it challenging to train them in such a setting. Whereas the two aforementioned works propose network architectures, our third project proposes a dataset and benchmark called BURST that aims to unify the current, fragmented landscape of datasets in video segmentation research. BURST includes a benchmark suite that evaluates multiple tasks related to object segmentation in video with shared data and consistent evaluation metrics. The idea behind this is to facilitate knowledge exchange between the research sub-communities tackling these tasks and also to encourage the development of methods with multi-task capability. Finally, our fourth work, TarViS, can be seen as a logical continuation of the above in that it is a method that can tackle multiple video segmentation tasks. To achieve this, we decouple the task definition from the core network architecture and use a set of dynamic query inputs to specify the task-specific segmentation targets. This formulation enables us to train a single model jointly on a collection of datasets spanning multiple tasks (Video Instance/Object/Panoptic Segmentation). During inference, the model can switch between tasks by simply hot-swapping the input queries accordingly. Es laden ein: die Dozentinnen und Dozenten der Informatik

4 Monate, 4 Wochen

Einladung Informatik-Oberseminar Jan Rosendahl, 22. Februar 2024,

von Stephanie Jansen

********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * *********************************************************************** Zeit: Donnerstag, 22. Februar 2024, 14:00 Uhr Ort: Raum 9222, E3, Informatikzentrum Zoom:https://rwth.zoom-x.de/j/67896121061?pwd=RlJTNUw5RFJYU0NwMVRWNEhvQ0EwZz09 Meeting-ID: 678 9612 1061 Kenncode: 493665 Referent: Jan Rosendahl, M.Sc.; Lehrstuhl Informatik 6 Thema: Attention-Based Machine Translation Using Monolingual Data Abstract: Neural networks present a major advance in modeling for statistical machine translation systems. In this dissertation, we focus on two central aspects of neural machine translation systems, namely the training data and the attention layer that connects the encoder and decoder. The parameters of a neural machine translation system are determined by minimizing the cross-entropy loss on a corpus of bilingual training data, i.e. a set of sentence pairs where one is the translation of the other. Since such sentence-aligned bilingual data is a scarce resource and availability depends on the language pair, we investigate using monolingual data to improve the performance of the machine translation system (Methods used: language model integration, monolingual pre-training, and back-translation). Inspired by existing work on alignment models, we also incorporate a first-order dependency in the encoder-decoder attention layer. In contrast with previous machine translation models, the transformer is a pure feed-forward model without any recurrent layers. That means that no information about the previous attention decision is input to the computation of the attention layer. Modeling attention with first-order dependencies allows the attention layer to access previous attention decisions, which is a prerequisite to express, e.g. source coverage. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Stephanie Jansen Faculty of Mathematics, Computer Science and Natural Sciences Chair of Computer Science 6 ML - Machine Learning and Reasoning RWTH Aachen University Theaterstraße 35-39 D-52062 Aachen Tel: +49 241 80-21601 sek(a)ml.rwth-aachen.de www.hltpr.rwth-aachen.de

5 Monate, 1 Woche

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informatik-vortraege Februar 2024