- informatik-vortraege - lists.rwth-aachen.de

Einladung: Informatik-Oberserminar Theodora Kontogianni

von Theodora Kontogianni

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 29. November 2021, 14.00 Uhr Ort: Zoom Videokonferenz https://rwth.zoom.us/j/93845227037?pwd=cm9qRjhtVm5JbWRYdGkrSUsyRythdz09 <https://www.google.com/url?q=https://rwth.zoom.us/j/93845227037?pwd%3Dcm9qR…> Meeting ID: 938 4522 7037 Passcode: 310833 Referent: Theodora Kontogianni, M.Sc. Lehrstuhl Informatik 13 Thema: Object Discovery, Interactive and 3D Segmentation for Large-Scale Computer Vision Tasks Abstract: In this talk, I present my thesis contributions that deal with issues arising when trying to exploit the large body of data available for computer vision tasks. In particular we address the problem of unsupervised object discovery in time-varying, large-scale image collections by proposing a novel tree structure that closely approximates the Minimum Spanning Tree and present an efficient construction approach along with an incremental update mechanism of the tree structure that incorporates new data as they are added to the image database. We then focus on defining novel 3D convolutional and recurrent operators over unstructured 3D point clouds. The goal is to learn point representations for the task of 3D semantic segmentation. We overcome the limitations of the unstructured and large-scale nature of the 3D point clouds by defining local structure through two clustering methods and expand the limited receptive field of previous approaches by modeling long-range relationships with the use of Recurrent Networks. In the third part, we address the task of interactive object segmentation where a computer vision algorithm segments an object aided by a human user. We present a method that significantly reduces the number of required user clicks compared to previous works. We use the sparse user corrections to adapt the model parameter on-the-fly during test time. In particular, we look at out-of-domain settings where the test datasets are significantly different from the datasets used to train our deep learning model. Es laden ein: die Dozentinnen und Dozenten der Informatik

2 Jahre, 8 Monate

1
0
0 0

Einladung: Informatik-Oberseminar Herr Martin Liebenberg

von Leany Maaßen

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 22. November 2021, 10:00-11:00 Uhr Zoom: https://rwth.zoom.us/j/95217813154?pwd=RkQ1ZTllbi94OUZiZDRNRE15eGpHZz09 Meeting ID: 952 1781 3154 Passcode: 596536 Referent: Herr Dipl.-Inform. Martin Liebenberg LuFG Informatik 5 Thema: Autonomous Agents for the World Wide Lab Artificial Intelligence in the Manufacturing Industry Abstract: The Internet of Production (IoP) is a research programme, where 30 interdisciplinary institutes work on revolutionising the manufacturing industry. A central concept of the IoP is the World Wide Lab (WWL) by which in a lab of labs the data of many manufacturing processes should be made available as if the data came from ones own manufacturing processes. With this data, which we receive from the WWL, we want to build Digital Shadows that are condensed or aggregated data for a specific purpose, such as a reduced mathematical model or a trained neural network. An early vision of the usage of the IoP is a Google-like web search, where one can pose a manufacturing problem and get in return an answer with which one can improve ones production process or build new products. In my thesis, I propose a solution to realise such a scenario based on Artificial Intelligence (AI) methods, which I call WWL Agents. Inspired by the ideas of the Semantic Web, these agents should automate the search for data, knowledge or Digital Shadows in the WWL for specific manufacturing problems, which we think is impractical to do manually. Furthermore, WWL Agents should apply the found information to build Digital Shadows or improve manufacturing processes. In this talk, we present the development of WWL Agents from three different perspectives. First, we consider it from the perspective of building Digital Shadows in a cross-domain collaboration. The second perspective relates to modelling the behaviour of WWL agents. Finally, we discuss the infrastructure required by a WWL Agent to provide semantic interoperability in the WWL. By these means we obtain a powerful concept by which the user can get the precise meaning of an answer and, through provenance information, knowledge about the origin of entities of the answer. Moreover, we demonstrate applications for WWL Agents in manufacturing in one exemplary use case where the agents plan production processes. In hot rolling, we show that, with local search, agents can find very quickly schedules, which could be used to repair failed rolling schedules during operation. 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

2 Jahre, 8 Monate

1
0
0 0

Einladung: Informatik-Oberseminar Svenja Noichl

von Lehr- und Forschungsgebiet Informatik 9

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 4. Oktober 2021, 10.00 Uhr Ort: Zoom-Videokonferenz (https://rwth.zoom.us/j/93693721093?pwd=OWo0eXJaajgram9lY1hxUVE1N0lXZz09) Referent: Svenja Noichl M.Sc. Lehr- und Forschungsgebiet Informatik 9 Thema: InfoBiTS: Informatische Bildung für Technikferne Seniorinnen und Senioren Abstract: Digitale Kompetenzen gewinnen im Zuge der fortschreitenden Digitalisierung in allen Bereichen des alltäglichen Lebens zunehmend an Relevanz. Dies gilt auch für ältere Personen, welche bisher wenig oder keine Berührungspunkte mit digitalen Technologien hatten. Unter Digitalkompetenz oder digitaler Kompetenz wird hier die Kombination aus Medienkompetenz und Informatikkompetenz verstanden, also die Kombination aus Aspekten der Medienkunde, Medienkritik, Mediennutzung und Mediengestaltung sowie Grundkenntnissen über unter anderem die Funktionsweisen von Informatiksystemen. Während in bestehenden Angeboten für ältere Menschen, wie z. B. Computer-, Smartphone- oder Tabletkursen, bei Peer-Learning Angeboten oder bei Technikbegleitung und Sprechstunden, zumeist die Medienkompetenz adressiert wird, soll das hier entwickelte Angebot einen größeren Fokus auf die Informatikkompetenz legen. Durch die Vermittlung von Ideen und Konzepten aus der Informatik, soll so über die reine Nutzungskompetenz digitaler Endgeräte hinaus, übertragbares Wissen dieser Domäne gefördert werden. Die Zielgruppe sind Personen ab 50 Jahren, welche keine bis wenig Vorerfahrung mit digitalen Technologien besitzen. Um den Lernerfolg in dem für die Zielgruppe neuen Gebiet bestmöglich zu unterstützen, ist die Berücksichtigung der Geragogik unerlässlich. Drei wichtige Aspekte stellen hierbei (1) das Lernen mit Gleichgesinnten, (2) das Lernen in einem geschützten Raum sowie (3) schnelle Hilfe bei Fragen und Problemen dar. Das hier entwickelte Angebot setzt daher nicht auf eine alleinige Nutzung der entwickelten Lernapp (InfoBiTS), sondern bettet diese in ein Kurskonzept ein. Bewährt hat sich dafür ein Workshopsetting. Ein Onlinesetting ist mit Einschränkungen ebenfalls möglich. Die InfoBiTS-App beinhaltet vier Lernmodule, welche sich mit den Themen Kommunikation, Funktionsweise des Internets, mobile Geräte und das Internet sowie Datenschutz und Datensicherheit befassen. Die Module adressieren hierbei jeweils Kompetenzen aus dem Curriculum für Seniorinnen und Senioren, welches im Rahmen dieser Arbeit entwickelt wurde und auf nationalen und internationalen Schulcurricula sowie Interessen der Zielgruppe, welche in einem Fragebogen mit 123 Teilnehmenden erhoben wurden, basiert. Für die konkrete Themenauswahl waren darüber hinaus Themen aktueller Relevanz sowie Anknüpfungspunkte an den Alltag der Zielgruppe, z. B. die Kommunikation mit (entfernt lebenden) Kindern und Enkeln, maßgeblich. Während die Pilotstudie im Workshopsetting durchgeführt wurde, erfolgte die Evaluation, aufgrund der Einschränkungen durch die vom Coronavirus SARS-CoV-2 ausgelöste Pandemie, im Onlinesetting. Insgesamt nahmen 19 Personen zwischen 50 und 84 Jahren teil. Die Evaluation zeigte insbesondere, dass sich das Gefühl der Kontrolle im Umgang mit und über die Technik im Vergleich zu vor dem ersten Modul und nach dem letzten Modul signifikant verbesserte. Weiterhin deuten die Ergebnisse einer modulbezogenen Selbsteinschätzung sowie die Bearbeitung der Aufgaben innerhalb der Module darauf hin, dass die angestrebten Lernziele in den Modulen weitestgehend erreicht werden konnten, was auf eine Förderung der adressierten Kompetenzen aus dem Curriculum hindeutet. Letztlich weißt die Auswertung des DigComp 2.1, dem europäischen Referenzrahmen für digitale Kompetenzen, nicht nur auf eine Verbesserung der erwarteten Kompetenzen hin, sondern auch auf eine Verbesserung weiterer Kompetenzen, wie beispielsweise im Bereich des Umgangs mit technischen Problemen. Es laden ein: die Dozentinnen und Dozenten der Informatik

2 Jahre, 10 Monate

1
0
0 0

Einladung: Informatik-Oberseminar Dimitri Bohlender

von Bohlender, Dimitri

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 23. September 2021, 11.00 Uhr Zoom: https://rwth.zoom.us/j/92021395779?pwd=ckRtKzJhcHFmeWxaU3YxSEZRWTJTUT09 Meeting-ID: 920 2139 5779 Kenncode: 534446 Referent: Dimitri Bohlender M.Sc. Lehrstuhl Informatik 11 Thema: Symbolic Methods for Formal Verification of Industrial Control Software Abstract: Many of the systems that we rely on, and interact with on a daily basis, are driven by software. Unfortunately, design and implementation of such systems is naturally prone to error, as it is done by humans and involves reasoning about the vast number of states a system may reach. While testing is the common approach to alleviating the risk of writing faulty software, it can only help with finding errors, but not prove their absence. By way of contrast, formal methods have mathematical foundations and enable rigorous reasoning about the behaviour of formally modelled systems. In particular, they give rise to formal verification procedures for proving a system's compliance with certain formal specifications. Although many such procedures can simplistically be thought of as an automatic exploration of a system's state space, the explicit enumeration of each reachable state can often be avoided. To this end, symbolic methods reason about many states at a time by representing sets of states and transition relations as logical formulas. My thesis is concerned with advances in symbolic methods for formal verification of the software-driven reactive systems that are used in the setting of industrial automation. While these systems often operate in safety-critical environments, the specifications and peculiarities of the domain impede the use of existing verification machinery for general-purpose programming languages, leaving engineers in need of computer-aided reasoning about the control software semantics. Our contributions address this issue in platform-agnostic ways, but are presented using the example of programmable logic controllers which are tailored to the industrial automation domain and therefore widely used. In this talk, we give an overview over our contributions with a focus on the approaches that leverage constrained Horn clause solving. After a short introduction, we sketch how a logical characterisation of control software safety in terms of constrained Horn clauses can be derived from reactive systems safety foundations. To exploit the modularity of control software, the characterisation is also extended and combined with mode abstraction – a domain-specific analysis for approximating the state space. Furthermore, we present approaches for the design and verification of control software that is resilient to potential restarts of the controller. We show how the choice of persistent variables can be reduced to parameter synthesis, and solved by extending the previous verification procedures. Es laden ein: die Dozent*innen der Informatik

2 Jahre, 10 Monate

1
0
0 0

Einladung: Informatik-Oberserminar Paul Voigtlaender

von Voigtlaender, Paul

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 24. September 2021, 09.00 Uhr Ort: Zoom Videokonferenz https://rwth.zoom.us/j/98629457476?pwd=WHB2U0FUMzVTaWU2S0paeUJESy8vdz09 Meeting ID: 986 2945 7476 Passcode: 073217 Referent: Paul Voigtlaender, M.Sc. Lehrstuhl Informatik 13 Thema: Video Object Segmentation and Tracking Abstract: Video Object Segmentation (VOS) is the computer vision task of segmenting generic objects in a video given their ground truth segmentation masks in the first frame. Strongly related are the tasks of single-object tracking (SOT) and multi-object tracking (MOT), where one or multiple objects need to be tracked on a bounding box level. All these tasks are highly related and have important applications like autonomous driving and video editing. At the same time, all of these tasks remain very challenging till today. In this talk, we present our work on VOS, MOT, and SOT. Firstly, we present a VOS method, FEELVOS, which follows the feature embedding-learning paradigm. FEELVOS is one of the first VOS methods which use a feature embedding as internal guidance of a convolutional network and learn the embedding end-to-end with a segmentation loss. Following this approach, FEELVOS achieves strong results while being fast and not requiring test-time fine-tuning. This feature embedding-learning paradigm together with end-to-end learning has by now become the dominating approach for VOS. We further extend the popular MOT task to Multi-Object Tracking and Segmentation (MOTS) by requiring methods to also produce segmentation masks. We propose a semi-automatic labeling method and use it to annotate two existing MOT datasets with masks. We release the resulting KITTI MOTS and the MOTSChallenge benchmarks together with new evaluation measures and a baseline method. Additionally, we promote the new MOTS task by hosting a workshop challenge. MOTS is a step towards bringing the communities of VOS and MOT together to facilitate further exchange of ideas. Finally, we present Siam R-CNN, a Siamese re-detection architecture based on Faster R-CNN, to tackle the task of long-term single-object tracking. In contrast to most previous long-term tracking approaches, Siam R-CNN performs re-detection on the whole image instead of a local window, allowing it to recover after losing the object of interest. Additionally, we propose a tracklet dynamic programming (TDPA) algorithm to incorporate spatio-temporal context into Siam R-CNN. Siam R-CNN produces strong results for SOT and VOS, and performs especially well for long-term tracking.

2 Jahre, 10 Monate

1
0
0 0

Einladung: Informatik-Oberseminar Martin Ritzert

von Martin Ritzert

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 23. September 2021, 14.00 Uhr Ort: Zoom-Videokonferenz und Raum 9222 Für die Teilnahme vor Ort wird um Anmeldung gebeten Link: https://rwth.zoom.us/j/95250295111?pwd=cjhFMGk0bStZcGNvcmYrYURPWnQ0dz09 Referent: Martin Ritzert M.Sc. Lehrstuhl für Informatik 7 Thema: Learning on Graphs with Logic and Neural Networks Abstract: In the domain of graphs we show strong connections between logic and machine learning in both theory and practice. In a purely theoretical framework we develop sublinear machine learning algorithms for supervised learning of logical formulas on various graph classes. Further we show that learning first-order logic on arbitrary graphs is intractable unless P=NP. At the intersection of theory and practice, we prove an equivalence between graph neural networks and the 1-dimensional Weisfeiler-Leman algorithm. As a practical application, we approximate combinatorial problems with recurrent graph neural networks. The proposed architecture is unsupervised and can be applied to all maximum constraint satisfaction problems. Es laden ein: die Dozentinnen und Dozenten der Informatik

2 Jahre, 10 Monate

1
0
0 0

Einladung: Informatik-Oberseminar Vipin Ravindran Vijayalakshmi

von Bolke-Hermanns, Helene

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 6. September 2021, 10:00 Uhr Zoom: https://rwth.zoom.us/j/91549249969?pwd=K1VGbDhkaUlpUDdhSFR3dXh1aWFzQT09 Meeting ID: 915 4924 9969 Passcode: 391915 Referent: Vipin Ravindran Vijayalakshmi, M. Sc. Lehrstuhl für Management Science Thema: Selfishness in Strategic Resource Allocation Problems Abstract: Non-cooperative game theory has emerged as an essential tool in analyzing and predicting the outcome of decentralized systems. Various algorithmic aspects arising due to strategic behavior among multiple non-cooperative users in several classes of resource allocation problems can be studied using a game theoretic approach. In this talk, we consider congestion games which are often used to model various scenarios of resource allocation by non-cooperative users. Congestion games constitute of a class of games in which a pure Nash equilibrium always exists. The hardness of computing a pure Nash equilibrium in congestion games has been of significant interest in the scientific community over the past two decades. As computing an exact pure Nash equilibrium is known to be hard, we study a weaker notion of pure Nash equilibrium called an approximate pure Nash equilibrium and to that extend, analyze an efficient algorithm that computes approximate pure Nash equilibria in congestion games with an improved approximation guarantee. In spite of being the predominant class of games to model resource allocation problems involving different users, congestion games lack an element of time dependence, especially in certain application scenarios such as the road transportation network. It is quite unnatural to assume that all users of a road section experience the same congestion. We consider a scheduling game on parallel machines, in which jobs try to minimize their completion time by choosing a machine to be processed on. Each machine uses an individual priority list to decide on the order according to which the jobs on the machine are processed. Here, we study the existence of a pure Nash equilibrium and characterize classes of instances in which a pure Nash equilibrium is guaranteed to exist. Es laden ein: die Dozentinnen und Dozenten der Informatik

2 Jahre, 11 Monate

1
0
0 0

Einladung: Informatik-Oberseminar Peter Lindner

von Peter Lindner

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 24. August 2021, 10:00 Uhr Zoom: https://rwth.zoom.us/j/97621241861?pwd=TURveUs0YVRWck5XRWprNEJCdFJmZz09 Meeting ID: 976 2124 1861 Passcode: 592961 Referent: Peter Lindner, M. Sc. Lehrstuhl für Informatik 7 (Logik und Theorie diskreter Systeme) Thema: The Theory of Infinite Probabilistic Databases Abstract: Probabilistic (relational) databases extend the conventional relational database model by probability distributions over database instances. Such models are desirable for, and applicable to, a wide range of scenarios where data is subject to uncertainty. For a long time, the theoretical state of the art was to view a probabilistic database as a discrete probability space with only finitely many possible outcome instances. In this talk, we give an overview over our contributions regarding the extension of this model, along with various key concepts, to infinite probability spaces. This is required to support basic probabilistic methods, like continuous noise models and tackles default shortcomings of the restriction to discrete spaces. To this end, we present and explore a suitable unifying framework for infinite probabilistic databases that is based on point processes. This covers all applications of interest, and complies with the desired requirements for such a framework. It's inherent abstractness allows us to discuss central notions from the theory of probabilistic databases like query semantics and independence assumptions from a plain probability theoretic point of view. Es laden ein: die Dozentinnen und Dozenten der Informatik

2 Jahre, 11 Monate

1
0
0 0

Einladung: Informatik-Oberserminar Francis Engelmann

von Francis Engelmann

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 16. August 2021, 14.00 Uhr Ort: Zoom Videokonferenz https://rwth.zoom.us/j/97522924662?pwd=NWN1Z2lkWGJOTkt0N2QyTHBRdSs1Zz09 <https://www.google.com/url?q=https://rwth.zoom.us/j/97522924662?pwd%3DNWN1Z…> Meeting ID: 975 2292 4662 Passcode: 451032 Referent: Francis Engelmann, M.Sc. Lehrstuhl Informatik 13 Thema: 3D Scene Understanding on Point Clouds Abstract: In this talk I present my thesis contributions to the emerging field of 3D scene understanding. That is, given a 3D scene representation as input, we address tasks such as 3D object detection, shape reconstruction and pose estimation, as well as 3D semantic- and instance-segmentation. The recent availability of inexpensive depth sensors has made 3D data widely accessible. At the same time, current aspirations in the field of robotics, augmented reality and self-driving cars require efficient and reliable algorithms for understanding different 3D scene representations, such as polygon meshes, point clouds or volumetric structures. While 3D data overcomes inherent limitations of projected 2D views, such as occlusions, scale-ambiguity and lack of geometry, it also introduces new challenges including sparsity and non-uniform sampling. Therefore, existing methods for 2D image processing do not generalize well to 3D data structures. In this talk, we present novel approaches specific to 3D scene understanding. The main contributions are organized into three parts: The core contribution of the first part is a probabilistic formulation which integrates 3D shape and motion priors as well as stereo depth measurements into a global optimization problem. The resulting approach can jointly estimate the 3D shape, pose and motion of multiple vehicles in urban street scenes. The second part deals with new deep learning models for processing 3D point clouds. In particular, we propose sequential and recurrent consolidation units for increasing the spatial context of point networks, and a simple yet efficient dilation mechanism for increasing the receptive field size of deep point convolutional networks. Finally, in the third part, we introduce advanced deep learning models. For semantic segmentation, we present the combination of two types of convolutions operating jointly on point clouds and mesh surfaces. In instance segmentation, we propose a new paradigm combining bottom-up and top-down approaches as introduced in previous works. The talk concludes with a discussion and directions for future research.

2 Jahre, 11 Monate

1
0
0 0

UnRAVeL "Behind the Scenes" Survey Lecture

von Tim Seppelt

Dear all, part of the programme of the research training group UnRAVeL is a series of introductory lectures on the topics of „randomness“ and „uncertainty“ in UnRAVeL’s research thrusts algorithms and complexity, verification, logic and languages, and their application scenarios. Each lecture is delivered by one of the researchers involved in UnRAVeL. The main aim is to provide doctoral researchers as well as master students a broad overview of the subjects of UnRAVeL. This year, 12 UnRAVeL professors will answer the following questions, based on one of their recent scientific results: * How did you get to this result? * How did you come up with certain key ideas? * How did you cope with obstacles on the way? Which ideas you had did not work out? Following these talks, PhD students will give an informal summary of their doctoral studies within UnRAVeL. All interested doctoral researchers and master students are invited to attend the UnRAVeL lecture series 2021 and engage in discussions with researchers and doctoral students. Details information can be found on https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-… All events take place on *Thursdays from 16:30 to 18:00 on Zoom* https://rwth.zoom.us/j/96043715437?pwd=U0dRczkyQjRCY21abW13TDNmUHlhUT09 * 15/04/2021 Survey Lecture: Erika Ábrahám: Probabilistic Hyperproperties * 22/04/2021 Jürgen Giesl: Inferring Expected Runtimes of Probabilistic Programs * 29/04/2021 Erich Grädel: Hidden Variables in Quantum Mechanics and Logics of Dependence and Independence * 06/05/2021 Christof Löding: Learning Automata for Infinite Words * 20/05/2021 Martin Grohe: The Logic of Graph Neural Networks * 10/06/2021 Britta Peis: Sensitivity Analysis for Submodular Function Optimization with Applications in Algorithmic Game Theory * 17/06/2021 Nils Nießen: Optimised Maintenance of Railway Infrastructure * 24/06/2021 Gerhard Lakemeyer: Uncertainty in Robotics * 01/07/2021 Joost-Pieter Katoen: The Surprises of Probabilistic Termination * 08/07/2021 Christina Büsing: Robust Minimum Cost Flow Problem Under Consistent Flow Constraints * 15/07/2021 Ringvorlesung: Gerhard Woeginger: Bilevel optimization * 22/07/2021 Ulrike Meyer: Malware Detection We are looking forward to seeing you at the lectures. Best regards, Tim Seppelt for the organisation committee https://www.unravel.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaydoc…

2 Jahre, 12 Monate

1
13
0 0

2024

2023

2022

2021

2020

2019

2018

informatik-vortraege