January 2021 - informatik-vortraege

Einladung Informatik-Oberseminar Malte Nuhn
by Sekretariat I6 30 May '22

30 May '22

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

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[infprof-l] Einladung Informatik-Oberseminar Philipp Weidel, December 15, 2020
by Abigail Morrison 02 Mar '21

02 Mar '21

********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 15. Dezember 2020, 10:00 Uhr Zoom: https://rwth.zoom.us/j/99233095930?pwd=dHhTV253V1ZYUzRtSkk1L3A1REZVUT09 Meeting-ID: 992 3309 5930 Kenncode: 626162 Referent: Philipp Weidel, Dipl. Inform. Thema: Learning and decision making in closed loop simulations of plastic spiking neural networks Abstract: To understand how animals and humans learn, form memories and make decisions is a highly relevant goal both for neuroscience and for fields that take some inspiration from neuroscience, such as machine learning and artificial intelligence. Many models of learning and decision making were developed in the fields of machine learning, artificial intelligence, and computational neuroscience. Although these models aim to describe similar mechanisms, they do not all pursue the same goal. These models can be differentiated between models aiming to reach optimal performance on a specific task (or set of tasks) and models trying to explain how animals and humans learn. Some models of the first class use biologically inspired methods (such as deep learning) but are usually not biologically realistic and are therefore not well suited to explain the function of the brain. Models in the second class focus on being biologically plausible to explain how the brain works, but often demonstrate their capability on too simplistic tasks and yield low performance on well-known tasks from machine learning. This work aims to close the gap between these two types of models. In the first part of this talk, tools are described that allow the combination of biologically plausible neural network models together with powerful toolkits known from machine learning and robotics. To this end, MUSIC, the middleware for spiking neural network simulators such as NEST and NEURON is interfaced with ROS, a middleware for robotic hardware and simulators such as Gazebo. This toolchain is extended with interfaces to reinforcement learning toolkits such as the OpenAI Gym. The second part addresses the question of how the brain can represent its environment in the neural substrate of the cortex and how a realistic model of reinforcement learning can make use of these representations. To this end, a spiking neural network model of unsupervised learning is presented which is able to learn its input projections such that it can detect and represent repeating patterns. By using an actor-critic reinforcement learning architecture driven by a realistic dopamine modulated plasticity rule the model can make use of the representations and learn a range tasks. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Prof. Dr. Abigail Morrison IAS-6 / INM-6 / SimLab Neuroscience Jülich Research Center & Computer Science 3 - Software Engineering RWTH Aachen http://www.fz-juelich.de/inm/inm-6 http://www.fz-juelich.de/ias/jsc/slns http://www.se-rwth.de Office: +49 2461 61-9805 Fax # : +49 2461 61-9460 ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------ Forschungszentrum Juelich GmbH 52425 Juelich Sitz der Gesellschaft: Juelich Eingetragen im Handelsregister des Amtsgerichts Dueren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Volker Rieke Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender), Karsten Beneke (stellv. Vorsitzender), Prof. Dr.-Ing. Harald Bolt ------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------

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Einladung Informatik-Oberseminar Konrad Anton Fögen
by Zinner, Marion 26 Jan '21

26 Jan '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 10. Februar 2021, 14.00 Uhr Zoom: https://rwth.zoom.us/j/91716576115?pwd=ZzUwemxXWUJkQURjQjJibmlGb3dYZz09 Meeting ID: 917 1657 6115 Passcode: 496556 Referent: Konrad Anton Fögen M.Sc. Lehr- und Forschungsgebiet Softwarekonstruktion Thema: Combinatorial Robustness Testing based on Error-Constraints Abstract: In this talk, we present an extension to combinatorial testing (CT) which is an effective specification-based test method that is based on an input parameter model (IPM). We argue that robustness is an important property of a software, which must be tested in addition to a software's functionality. This requires invalid values and invalid value combinations to be able to observe a software's reaction to them. However, the effectiveness of CT deteriorates in the presence of invalid values or invalid value combinations. This phenomenon is called invalid input masking effect and is already acknowledged in some research. It led to extensions of CT that we call combinatorial robustness testing (CRT). The objective of CRT is to improve the fault detection by avoiding invalid input masking. This is achieved by separating the testing of valid values and valid value combinations from the testing of invalid values and invalid value combinations. While CRT is a promising extension of CT, it is still insufficiently researched. For instance, in related work, IPMs are extended with additional semantic information to specify invalid values. However, invalid value combinations cannot be specified directly. Therefore, the objective of this work is to further expand the idea of CRT. The aim is to develop a new CRT test method with a modeling approach to specify invalid values and invalid value combinations equally well. This modeling approach should also be incorporated into explicit test adequate criteria and test selection strategies. Furthermore, this modeling approach shall be supported by automated techniques. First, we conduct a controlled experiment to check if CRT is necessary at all or if CT is already appropriate to test robustness. Based on the result, we continue and develop a refined t-factor fault model that incorporates robustness faults and the inherent invalid input masking effect. Next, we develop a new test method for CRT and introduce a new structure that extends the structure of IPMs. It is called robustness input parameter model (RIPM) and contains the concept of error-constraints which is an additional set of logical expressions to describe the validity of values and value combinations. With the refined t-factor fault model and the new RIPM structure, new test adequacy criteria that incorporate the additional semantic information and new test selection strategies that satisfy the test adequacy criteria are developed. The new concept of error-constraints requires additional effort in modeling. Therefore, we develop two techniques to support the modeling of them. First, we develop a technique to identify and repair inconsistencies among error-constraints. Second, we develop a technique to automatically generate error-constraints based on the conformance to another system. Last but not least, all aforementioned concepts and techniques are operationalized and integrated in a test automation framework which includes a process, an architecture, and a Java-based reference implementation. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung Informatik-Oberseminar Oliver Kautz
by Kautz, Oliver 25 Jan '21

25 Jan '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 10. Februar 2021, 15.00 Uhr Zoom: https://rwth.zoom.us/j/96367107600?pwd=c2lMb2M1OXZXSHZFalRySUR2QTExUT09 Referent: Oliver Kautz M.Sc. Lehrstuhl Informatik 3 Thema: Model Analyses Based on Semantic Differencing and Automatic Model Repair Abstract: Models are the primary development artifacts used in model-driven software development. Therefore, models continuously evolve during the design, development, and maintenance of software systems. Thus, model differencing is an important task to understand the syntactic and semantic differences between model versions. Previous work produced general (and thus language-independent) approaches for syntactic model differencing, but only a few language-dependent approaches for semantic model differencing. Approaches combining syntactic with semantic model differencing by relating the syntactic changes of models to their semantic differences rarely exist. Previous work neglected the development of language-independent approaches abstracting from a concrete model property for detecting the syntactic elements of a model, which cause that the model does not satisfy the property. If the property encodes a requirement and the non-satisfaction represents the existence of a bug, then detecting the syntactic model elements causing the non-satisfaction of the property facilitates developers in detecting the syntactic model elements causing the bug. In this talk, we present a framework for precisely defining modeling languages, including syntax, semantics, and model evolution possibilities. We discuss syntactic and semantic model differencing. The framework is instantiated with four concrete modeling languages: Time-synchronous port automata, feature diagrams, sequence diagrams, and activity diagrams. Based on the framework for precisely defining modeling languages, we present a modeling language and property-independent framework for automatic model repairs. The framework facilitates developers in detecting the syntactic elements of a model causing that the model does not satisfy a property. Instantiating the framework with a concrete modeling language and a concrete model property enables the automatic calculation of syntactic changes that transform a model not satisfying the property to a model that satisfies the property. The framework relies on the assumption that it is possible to partition the syntactic changes applicable to each model into finitely many model-specific and property- specific equivalence classes. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung Informatik-Oberseminar Martin Serror
by Martin Serror 20 Jan '21

20 Jan '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 29. Januar 2021, 14.00 Uhr Zoom: https://rwth.zoom.us/j/95719946489?pwd=S0lITm9pcW45b1k4SW5EVis2a1poQT09 Referent: Martin Serror, M.Sc. Lehrstuhl Informatik 4 Thema: On the Benefits of Cooperation for Dependable Wireless Communications Abstract: The emerging Industrial Internet-of-Things (IIoT) improves flexibility, productivity, and costs of industrial processes by connecting sensors, actuators, and controllers to each other and the Internet. On the factory floor, such interconnections increasingly rely on wireless communications, reducing deployment and maintenance costs while supporting the mobility of communication partners. The industrial domain, however, is mainly characterized by safety- and mission-critical Machine-to-Machine communication. Therefore, state-of-the-art wireless communication protocols for home and business environments, such as WLAN and Bluetooth, are not suited for the IIoT. Consequently, the IIoT requires dependable wireless communication, achieving both high reliability and low latency. A promising approach for so-called Ultra-Reliable Low-Latency Communication (URLLC) in the IIoT is cooperative diversity, since the participating stations already collaborate toward a common goal, i. e., keeping the industrial process running. There, a sending station exploits multiple independent transmission paths via cooperating relays to convey a packet to its destination reliably. In contrast to spatial diversity, this approach also works with single-input single-output transceivers. However, when considering relaying for URLLC, it is particularly challenging that all participants have to share the scarce transmission resources. Hence, in this talk, we investigate various mechanisms enabling dependable wireless communication, i. e., increasing communication reliability within a bounded low latency, where we mainly focus on the benefits of cooperative diversity. Therefore, we explore different design options for URLLC and evaluate them, leveraging the advantages of different methodological approaches. This talk thus offers valuable insights into designing communication protocols with challenging requirements. At the example of cooperation, we thoroughly retrace the development process from analysis to prototypical deployment. On the one hand, the achieved results contribute to URLLC for the IIoT; on the other hand, they provide a critical examination of the selected evaluation methodologies. Es laden ein: die Dozentinnen und Dozenten der Informatik

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13.01.2020, 16:00: guest talk: Mahesh Viswanathan, University of Illinois at Urbana-Champaign: Verifying the Privacy and Accuracy ofAlgorithms for Differential Privacy
by Bolke-Hermanns, Helene-Maria 11 Jan '21

11 Jan '21

Happy new year, You are cordially invited to the next UnRAVeL guest talk on Wednesday, 13.01.2021, 16.00: Mahesh Viswanathan, University of Illinois at Urbana-Champaign: Verifying the Privacy and Accuracy of Algorithms for Differential Privacy Differential privacy is a mathematical framework for performing privacy-preserving computations over sensitive data. One important feature of differential privacy algorithms is their ability to achieve provable individual privacy guarantees and at the same time ensure that the outputs are reasonably accurate. Such algorithms compute noisy versions of the right answers to aggregate queries on sensitive data to ensure privacy. Privacy guarantees demand that the algorithm running on "similar" data sets produce responses that are statistically similar; this provides a very strong form of individual privacy. Accuracy, on the other hand, demands that the algorithms output, though noised, be sufficient close to the correct answer for a query. In this talk we will present preliminary results on the algorithmic complexity of checking the privacy and accuracy requirements for a given algorithm. Joint work with Giles Barthe, Rohit Chadha,Vishal Jagannath, Paul Krogmeier, and Prasad Sistla. Based on papers in LICS2020 and POPL 2021. Wednesday, 13.01.2020, 16:00 (!); https://www.unravel.rwth-aachen.de/go/id/kfruu https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…> Meeting ID: 920 4794 9381 Password: unravel Best regards Helen Bolke-Hermanns Helen Bolke-Hermanns RTG UnRAVeL - RWTH Aachen University Ahornstr. 55, D-52074 Aachen Building E3, 2nd floor, Room 9218 Telefon: +49 (241) 80-21 004 Fax: +49 (241) 80-22 215 E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de> Web: www.unravel.rwth-aachen.de<http://www.unravel.rwth-aachen.de/> [rwth_unravel_en_rgb]

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Einladung: Informatik-Oberseminar Isaak Lim
by Isaak Lim 06 Jan '21

06 Jan '21

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 21. Januar 2021, 15.00 Uhr Zoom: https://rwth.zoom.us/j/98847090675?pwd=TnA5L3NCTkx0TjBmMWJEZHZnQkx1QT09 Meeting-ID: 988 4709 0675 Kenncode: 904296 Referent: Isaak Lim M.Sc. Lehrstuhl Informatik 8 Thema: Learned Embeddings for Geometric Data Abstract: Solving high-level tasks on 3D shapes such as classification, segmentation, vertex-to-vertex maps or computing the perceived style similarity between shapes requires methods that are able to extract the necessary information from geometric data and describe the appropriate properties. Constructing functions that do this by hand is challenging because it is unclear how and which information to extract for a task. Furthermore, it is difficult to determine how to use the extracted information to provide answers to the questions about shapes that are being asked (e.g. what category a shape belongs to). To this end, we propose to learn functions that map geometric data to an embedding space. The outputs of those maps are compressed encodings of the input geometric data that can be optimized to contain all necessary task-dependent information. These encodings can then be compared directly (e.g. via the Euclidean distance) or by other fairly simple functions to provide answers to the questions being asked. Neural networks can be used to implement such maps and comparison functions. This has the benefit that they offer flexibility and expressiveness. Furthermore, information extraction and comparison can be automated by designing appropriate objective functions that are used to optimize the parameters of the neural networks on geometric data collections with task-related meta information provided by humans. We therefore have to answer two questions. Firstly, given the often irregular nature of representations of 3D shapes, how can geometric data be represented as input to neural networks and how should such networks be constructed? Secondly, how can we design the resulting embedding space provided by neural networks in such a manner that we are able to achieve good results on high-level tasks on 3D shapes? In this talk we provide answers to these two questions. Concretely, de- pending on the availability of the data sources and the task specific requirements we compute encodings from geometric data representations in the form of images, point clouds and triangle meshes. Once we have a suitable way to encode the input, we explore different ways in which to design the learned embedding space by careful construction of appropriate objective functions that extend beyond straightforward cross-entropy minimization based approaches on categorical distributions. We show that these approaches are able to achieve good results in both discriminative as well as generative tasks. Es laden ein: die Dozentinnen und Dozenten der Informatik

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