informatik-vortraege April 2020

informatik-vortraege@lists.rwth-aachen.de

3 Teilnehmer
4 Diskussionen

Einladung Informatik-Oberseminar Malte Nuhn

von Sekretariat I6

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 12. Juli 2019, 10.00 Uhr Ort: Informatikzentrum, E3, Raum 9222 Referent: Dipl.-Inform. Malte Nuhn Thema: Unsupervised Training with Applications in Natural Language Processing// Abstract: The state-of-the-art algorithms for various natural language processing tasks require large amounts of labeled training data. At the same time, obtaining labeled data of high quality is often the most costly step in setting up natural language processing systems.Opposed to this, unlabeled data is much cheaper to obtain and available in larger amounts.Currently, only few training algorithms make use of unlabeled data. In practice, training with only unlabeled data is not performed at all. In this thesis, we study how unlabeled data can be used to train a variety of models used in natural language processing. In particular, we study models applicable to solving substitution ciphers, spelling correction, and machine translation. This thesis lays the groundwork for unsupervised training by presenting and analyzing the corresponding models and unsupervised training problems in a consistent manner.We show that the unsupervised training problem that occurs when breaking one-to-one substitution ciphers is equivalent to the quadratic assignment problem (QAP) if a bigram language model is incorporated and therefore NP-hard. Based on this analysis, we present an effective algorithm for unsupervised training for deterministic substitutions. In the case of English one-to-one substitution ciphers, we show that our novel algorithm achieves results close to human performance, as presented in [Shannon 49]. Also, with this algorithm, we present, to the best of our knowledge, the first automatic decipherment of the second part of the Beale ciphers.Further, for the task of spelling correction, we work out the details of the EM algorithm [Dempster & Laird + 77] and experimentally show that the error rates achieved using purely unsupervised training reach those of supervised training.For handling large vocabularies, we introduce a novel model initialization as well as multiple training procedures that significantly speed up training without hurting the performance of the resulting models significantly.By incorporating an alignment model, we further extend this model such that it can be applied to the task of machine translation. We show that the true lexical and alignment model parameters can be learned without any labeled data: We experimentally show that the corresponding likelihood function attains its maximum for the true model parameters if a sufficient amount of unlabeled data is available. Further, for the problem of spelling correction with symbol substitutions and local swaps, we also show experimentally that the performance achieved with purely unsupervised EM training reaches that of supervised training. Finally, using the methods developed in this thesis, we present results on an unsupervised training task for machine translation with a ten times larger vocabulary than that of tasks investigated in previous work. Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________________________ -- -- Stephanie Jansen Faculty of Mathematics, Computer Science and Natural Sciences HLTPR - Human Language Technology and Pattern Recognition RWTH Aachen University Ahornstraße 55 D-52074 Aachen Tel. Frau Jansen: +49 241 80-216 06 Tel. Frau Andersen: +49 241 80-216 01 Fax: +49 241 80-22219 sek(a)i6.informatik.rwth-aachen.de www.hltpr.rwth-aachen.de Tel: +49 241 80-216 01/06 Fax: +49 241 80-22219 sek(a)i6.informatik.rwth-aachen.de www.hltpr.rwth-aachen.de

1 Jahr, 11 Monate

Einladung: Informatik-Oberseminar Herr István Koren

von Leany Maaßen

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 08. Mai 2020, 11.00 Uhr Zoom: https://rwth.zoom.us/j/94637532047?pwd=aVlweVRLdGU0OWNjdEh4TmJhSktrUT09 Referent: Dipl.-Medieninf. István Koren Lehrstuhl Informatik 5 Thema: DevOpsUse: Community-Driven Continuous Innovation of Web Information Infrastructures Abstract: The steady evolution of the Web over the last thirty years was shaped by an interplay of new technologies and innovative applications. The current challenges are caused by the ongoing digital transformation of whole societies. In Industry 4.0 for example, these are changing workplace settings and the adoption of the Internet of Things. Inhibiting the demanded fast innovation cycles, this may create a disruptive and unstable environment in which the requirements of heterogeneous professional communities need to be addressed. Information systems infrastructure, while only partially visible and thus hard to grasp, has a strong influence on practices in professional communities. Therefore, our aim is to stabilize the dichotomies apparent in the Web by means of an agile information systems development methodology. It supports the evolution of infrastructure through community-driven and model-based technologies to guide it on a sustainable path of continuous innovation. Agile development practices in software engineering, in particular the already established DevOps approach, promote stronger cooperation between development and operating teams. Our DevOpsUse methodology additionally fosters a stronger involvement of end user communities in software development processes by including them in the process of infrastructuring, i.e. the appropriation of infrastructure during its usage. The developed DevOpsUse methodology has been successfully validated by the transitions between three generations of technologies: near real-time peer-to-peer Web architectures, edge computing, and the Internet of Things. In particular, we were able to demonstrate our methodologys capabilities through longitudinal studies in several large-scale international digitalization projects. Beyond Web information systems, the framework and its open source tools are applicable in further innovative areas like mixed reality. Its broad adaptability testifies that DevOpsUse has the potential to unlock sustainable innovation capabilities. 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. Dr. Matthias Jarke, Prof. Gerhard Lakemeyer Ph.D. Ahornstrasse 55 D-52074 Aachen Tel: 0241-80-21509 Fax: 0241-80-22321 E-Mail: maassen(a)dbis.rwth-aachen.de

3 Jahre, 12 Monate

Einladung: Informatik-Oberseminar Kazuki Irie

von Sekretariat I6

****************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * ******************************************************* Zeit: Dienstag, 05. Mai 2020, 14.00 Uhr Zoom: https://us02web.zoom.us/j/84813327259?pwd=Y2NydlRMRzE1dkpkcmpERkFwMWZYZz09 Referent: Kazuki Irie, M.Sc. Thema: Advancing Neural Language Modeling in Automatic Speech Recognition// Abstract: Statistical language modeling is one of the fundamental problems in natural language processing. In the recent years, language modeling has seen great advances by active research and engineering efforts in applying artificial neural networks, especially those which are recurrent. The application of neural language models to speech recognition has now become well established and ubiquitous. Despite this impression of some degree of maturity, we claim that the full potential of the neural network based language modeling is yet to be explored. In this thesis, we further advance neural language modeling in automatic speech recognition, by investigating a number of new perspectives. From the architectural view point, we investigate the newly proposed Transformer neural networks for language modeling application. The original model architecture proposed for machine translation is studied and modified to accommodate the specific task of language modeling. Particularly deep models with about one hundred layers are developed. We present an in-depth comparison with the state-of-the-art recurrent neural network language models based on the long short-term memory. While scaling up language modeling to larger scale datasets, the diversity of the data emerges as an opportunity and a challenge. The current state-of-the-art neural language modeling lacks a mechanism of handling diverse data from different domains for a single model to perform well across different domains. In this context, we introduce domain robust language modeling with neural networks, and propose two solutions. As a first solution, we propose a new type of adaptive mixture of experts model which is fully based on neural networks. In the second approach, we investigate knowledge distillation from multiple domain expert models, as a solution to the large model size problem seen in the first approach. Methods for practical applications of knowledge distillation to large vocabulary language modeling are proposed, and studied to a large extent. Finally, we investigate the potential of neural language models to leverage long-span cross-sentence contexts for cross-utterance speech recognition. The appropriate training method for such a scenario is under-explored in the existing works. We carry out systematic comparisons of the training methods, allowing us to achieve improvements in cross-utterance speech recognition. In the same context, we study the sequence length robustness for both recurrent neural networks based on the long short-term memory and Transformers, because such a robustness is one of the fundamental properties we wish to have, in neural networks with the ability to handle variable length contexts. Throughout the thesis, we tackle these problems through novel perspectives of neural language modeling, while keeping the traditional spirit of language modeling in speech recognition. Es laden ein: die Dozentinnen und Dozenten der Informatik -- -- Stephanie Jansen Faculty of Mathematics, Computer Science and Natural Sciences HLTPR - Human Language Technology and Pattern Recognition RWTH Aachen University Ahornstraße 55 D-52074 Aachen Tel. Frau Jansen: +49 241 80-216 06 Tel. Frau Andersen: +49 241 80-216 01 Fax: +49 241 80-22219 sek(a)i6.informatik.rwth-aachen.de www.hltpr.rwth-aachen.de Tel: +49 241 80-216 01/06 Fax: +49 241 80-22219 sek(a)i6.informatik.rwth-aachen.de www.hltpr.rwth-aachen.de

4 Jahre

Vortrag Matthias Wichtlhuber (DeCIX) 21.4. 11:00

von Johannes Krude

Hallo, Matthias Wichtlhuber vom DeCIX hält nächsten Dienstag für die Aachener Informatik einen Vortrag über Gegenmaßnahmen zu Distributed-Denial-of-Service (DDoS) attacks am DeCIX und über die Traffic Veränderungen am DeCIX aufgrund des COVID-19 lockdowns. Wann: Dienstag 24.4. 11:00 Zoom Meeting ID: 916 2538 0906, Password: 091338 https://rwth.zoom.us/j/91625380906?pwd=cEJCSk01VkVEUUpTSjgzYTlnNk5Odz09 Abstract des Vortrags: We will start with a short introduction of Internet Exchange Points (IXPs) and their operations. With this background, we will discuss the topic of Distributed-Denial-of-Service (DDoS) attacks at IXPs. We present results of a recent measurement study of DDoS-for-hire Websites (Booters) and the traffic effects of a seizure operation of 15 Booter websites by the FBI in late 2018 [1]. Subsequently, we introduce the concept of Advanced Blackholing, a published and operational mechanism designed by the DE-CIX research team to defend IXP links against DDoS attacks [2]. Due to the current COVID-19 situation, we will close the presentation by showing some preliminary results on the traffic shifts caused by nation-wide lockdowns in several countries. Über Matthias Wichtlhuber: Matthias Wichtlhuber holds a Diploma in Information Systems from Universität Mannheim and a Ph. D. from Technische Universität Darmstadt. His Ph. D. thesis focused on optimizing content delivery on the Internet. During his Ph. D. he worked on numerous EU and nationally funded research projects as well as industrial projects. After his studies, he joined DE-CIX, the operator of the largest Internet Exchange Point (IXP) in the world in Frankfurt. He is a member of the DE-CIX research team and works on product development, system security, future network architectures for Internet exchange points, and large-scale network data analysis. [1] Kopp, D., Wichtlhuber, M., Poese, I., Santanna, J., Hohlfeld, O., & Dietzel, C.: “DDoS Hide & Seek: On the Effectiveness of a Booter Services Takedown”, ACM IMC, 2019. [2] Dietzel, C., Wichtlhuber, M., Smaragdakis, G., & Feldmann, A.: “Stellar: Network Attack Mitigation using Advanced Blackholing”, ACM CoNEXT, 2018.

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informatik-vortraege April 2020