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Einladung zum Informatik Oberseminar von Herrn Hendrik Simon, M.Sc.

von Hendrik Simon

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 4. Mai 2023, 13.30 Uhr Ort: Seminarraum 2202, Hauptbau, Ahornstr. 55 Referent: Hendrik Simon M.Sc. Lehrstuhl Informatik 11 Thema: Automatic Test Case Generation for PLC Software Abstract: Automatic test case generation for the purpose of bug finding or achieving coverage goals has recently evolved to a scalable technique that is nowadays used to find highly critical security bugs, e. g. by Microsoft. However, in the domain of Programmable Logic Controllers (PLCs), applications of this technique are rare and usually rely on tools and mechanisms that were not initially designed for this domain. In fact, a discussion on how to design such techniques with the peculiarities of PLC software in mind, is missing. At the same time, PLC software is typically used in safety critical environments where software errors pose significant threats to the environment or humans and may additionally result in significant financial losses. Mature automatic testing techniques for the PLC domain would, thus, be highly beneficial to further support software quality in this area. PLC software typically follows a cyclic execution scheme that involves a repeated process of reading input values, executing a (often state machine based) control program that relies on local variables and writing computed values to outputs. Although the cyclic execution resembles only a small change in the execution semantics, the impact on automatic testing techniques is significant. This dissertation provides insights and mechanisms to transfer automatic test case generation into the domain of PLC software. We conduct an in-depth discussion on related approaches and point out strengths and weaknesses in order to provide baseline knowledge that can be utilised in future developments in this field of research. Further, we introduce our own automatic test case generation approaches and exemplify their effectiveness on PLC software. We are able to show that the generation of branch coverage tests can be achieved significantly faster than with existing techniques, rendering our approaches more applicable for larger software. The focus of our techniques lies in the exploitation of state-machine based execution behaviour and the preservation of structural information in Sequential Function Chart. For the latter, our presented algorithm can achieve full coverage in a few seconds for programs that could only partly be covered within an hour by related approaches. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 4 Monate

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Einladung: Informatik-Oberseminar Steffen van Bergerem

von Steffen van Bergerem

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 10. März 2023, 11.00 Uhr Ort: Raum 9222, Gebäude E3, Ahornstr. 55 Zoom: https://rwth.zoom.us/j/95679484581?pwd=Q0N4SUFZQ21jVERZMDAwc2cyQXpzZz09 Referent: Steffen van Bergerem, M.Sc. Lehrstuhl für Informatik 7 Thema: Descriptive Complexity of Learning Abstract: Supervised learning is a field in machine learning that strives to classify data based on labelled training examples. In the Boolean setting, each input is to be assigned to one of two classes, and there are several fruitful machine-learning methods to obtain a classifier. However, different algorithms usually come with different types of classifiers, e.g. decision trees, support-vector machines, or neural networks, and this is cumbersome for a unified study of the intrinsic complexity of learning tasks. This thesis aims at strengthening the theoretical foundations of machine learning in a consistent framework. In the setting due to Grohe and Turán (2004), the inputs for the classification are tuples from a relational structure and the search space for the classifiers consists of logical formulas. The framework separates the definition of the class of potential classifiers (the hypothesis class) from the precise machine-learning algorithm that returns a classifier. This facilitates an information-theoretic analysis of hypothesis classes as well as a study of the computational complexity of learning hypotheses from a specific hypothesis class. As a first step, Grohe and Ritzert (2017) proved that hypotheses definable in first-order logic (FO) can be learned in sublinear time over structures of small degree. We generalise this result to two extensions of FO that provide data-aggregation methods similar to those in commonly used relational database systems. First, we study the extension FOCN of FO with counting quantifiers. Then, we analyse logics that operate on weighted structures, which can model relational databases with numerical values. For that, we introduce the new logic FOWA, which extends FO by weight aggregation. We provide locality results and prove that hypotheses definable in a fragment of the logic can be learned in sublinear time over structures of small degree. To better understand the complexity of machine-learning tasks on richer classes of structures, we then study the parameterised complexity of these problems. On arbitrary relational structures and under common complexity-theoretic assumptions, learning hypotheses definable in pure first-order logic turns out to be intractable. In contrast to this, we show that the problem is fixed-parameter tractable if the structures come from a nowhere dense class. This subsumes numerous classes of sparse graphs. In particular, we obtain fixed-parameter tractability for planar graphs, graphs of bounded treewidth, and classes of graphs excluding a minor. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 5 Monate

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Einladung: Informatik-Oberseminar Herr Aleksandar Mitrevski

von Maaßen, Leany

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 30.01.2023, 13:00-14:00 Uhr Ort: Informatikzentrum, Ahornstraße 55, Raum 5053.2 (B-IT-Hörsaal) Referent: Herr Aleksandar Mitrevski, M. Sc. LuFG Informatik 5 Thema: Skill Generalisation and Experience Acquisition for Predicting and Avoiding Execution Failures Abstract: For performing tasks in their target environments, autonomous robots usually execute and combine skills. Robot skills in general and learning-based skills in particular are usually designed so that flexible skill acquisition is possible, but without an explicit consideration of execution failures, the impact that failure analysis can have on the skill learning process, or the benefits of introspection for effective coexistence with humans. Particularly in human-centered environments, the ability to understand, explain, and appropriately react to failures can affect a robot's trustworthiness and, consequently, its overall acceptability. Thus, in this dissertation, we study the questions of how parameterised skills can be designed so that execution-level decisions are associated with semantic knowledge about the execution process, and how such knowledge can be utilised for avoiding and analysing execution failures. The first major segment of this work is dedicated to developing a representation for skill parameterisation whose objective is to improve the transparency of the skill parameterisation process and enable a semantic analysis of execution failures. We particularly develop a hybrid learning-based representation for parameterising skills, called an execution model, which combines qualitative success preconditions with a function that maps parameters to predicted execution success. The second major part of this work focuses on applications of the execution model representation to address different types of execution failures. We first present a diagnosis algorithm that, given parameters that have resulted in a failure, finds a failure hypothesis by searching for violations of the qualitative model, as well as an experience correction algorithm that uses the found hypothesis to identify parameters that are likely to correct the failure. Furthermore, we present an extension of execution models that allows multiple qualitative execution contexts to be considered so that context-specific execution failures can be avoided. Finally, to enable the avoidance of model generalisation failures, we propose an adaptive ontology-assisted strategy for execution model generalisation between object categories that aims to combine the benefits of model-based and data-driven methods; for this, information about category similarities as encoded in an ontology is integrated with outcomes of model generalisation attempts performed by a robot. The proposed methods are evaluated in multiple experiments performed with a Toyota Human Support Robot. The main contributions of this work include a formalisation of the skill parameterisation problem by considering execution failures as an integral part of the skill design and learning process, a demonstration of how a hybrid representation for parameterising skills can contribute towards improving the introspective properties of robot skills, as well as an extensive evaluation of the proposed methods in various experiments. We believe that this work constitutes a small first step towards more failure-aware robots that are suitable to be used in human-centered environments. 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., 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

1 Jahr, 6 Monate

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Einladung Informatik-Oberseminar Marcus Völker

von Völker, Marcus

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 06. März 2023, 14:00 Uhr Ort: Raum 2202 (HBau, 2. Stock), Ahornstr. 55 Hybrid über Zoom: https://rwth.zoom.us/j/99712637671?pwd=U0UzV1JiL1hwWnlzNW5pUC9hVDdOUT09 Referent: Marcus Völker, M.Sc. RWTH Informatik 11 Embedded Software Thema: Policy Iteration for Value Set Analysis of PLC Programs Abstract: Ensuring the correct behaviour of computing systems is an important task to prevent danger to the users of such systems. To do this, many analysis techniques have been developed that can find bugs in software, or prove that it complies to some specification of correct behaviour. Among these techniques, a fundamental analysis is value set analysis (VSA), which can determine an approximation of the program variables' values at each point of the program. This is very important information, as many faulty behaviours can be traced back to variables taking unexpected values, such as division by zero, access to uninitialised memory or outside a buffer, or unreachable code. While classically, value set analysis is performed with the algorithm of Kleene iteration, another approach called policy iteration has been developed in recent years that provides an alternative with the potential of finding similar or better results than Kleene iteration in less time. Policy iteration works by using a heuristic to simplify the program in a certain way, finding the value sets of that program, and then checking whether the result is applicable to the original program. If yes, the results are used, otherwise different simplifications have to be checked, until a usable result is found. As policy iteration is a heuristic algorithm, it makes certain assumptions about program behaviour in order to achieve good results. It turns out, however, that these assumptions are not guaranteed if the program contains errors which cause it to behave differently than expected. As we use program analysis to find errors such as this, assuming an error-free program is not necessarily a good assumption. In this thesis, we show several ways to improve the original heuristic by focusing on program loops. First, we present a way to use a pre-analysis to determine some aspect of the loops' behaviours, and use this information in order to build a heuristic that leads to more accurate solutions than the standard heuristic in many cases, at the cost of additional running time necessary to perform this pre-analysis. Then, we show a way to reinterpret branches as loops if they occur in cyclical code, which is typical for programs in reactive systems, such as the systems used for factory automation. This allows us to use our loop heuristic on a wider variety of programs, even though the cost becomes even greater, and it is useful only in specific cases. Afterwards, we show how to remove the pre-analysis to gain back the lost time, while still retaining similarly good results to the expensive version introduced before. This has the additional benefit of allowing usage of the algorithms on branches as in the second approach, without incurring any additional costs. We motivate that this is the version of policy iteration that should be used in general with an extensive evaluation of generated programs. Finally, we show a way to analyse polynomial inequalities with value set analysis by reinterpreting them as conjunctions of simpler inequalities. This not only allows us to improve value set analysis results on programs that feature such inequalities, but also makes these programs accessible to policy iteration. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 6 Monate

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Einladung Informatik-Oberseminar Sascha Müller

von Noll, Thomas

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * +********************************************************************** Zeit: Donnerstag, 12. Januar 2023, 10:00 Uhr Ort: 9222 (Gebäude E3, Informatikzentrum) Referent: Sascha Müller M.Sc. DLR Braunschweig Thema: Synthesizing FDIR Recovery Strategies for Space Systems Abstract: This talk proposes an inherently non-deterministic model for Dynamic Fault Trees (DFTs) to analyze Fault Detection Isolation and Recovery concepts with a particular focus on the needs of space systems. Deterministic recovery strategies are synthesized by transforming these non-deterministic DFTs into Markov automata. From the corresponding scheduler, optimized to maximize a given RAMS metric, an optimal recovery strategy can then be derived and represented by a model we call recovery automaton. We discuss dedicated techniques for reducing the state space of this recovery automaton and investigate lifting the approach to a partially observable setting. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 6 Monate

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Einladung Informatik-Oberseminar: Beyond the rigid interfaces of

von Christos Psarras

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Monday, 23. January 2023, 15:30 Uhr Ort: Online (Zoom: https://umu.zoom.us/my/pauldj) Referent: Christos Psarras, M.Sc. International Research Training Group (IRTG-2379) Thema: Beyond the rigid interfaces of super-optimized building-block libraries. Our experiences in Chemometrics Abstract: The efficient computation of linear algebra expressions is a challenging task faced by many practitioners in scientific fields, such as engineering, image processing, and computational chemistry, to name a few. For most applications, mapping a target expression into a sequence of highly-optimized library routines (often referred to as "building-block" libraries, e.g., BLAS, LAPACK), is an approach that offers good computational performance as well as accuracy. However, in other applications, this approach inherently results in a vast under-utilization of the available computational resources, and thus reduced performance. In this talk, we emphasize on these, latter, applications, showcasing two occurrences that routinely arise in Chemometrics: the Canonical Polyadic Decomposition (CP) and Jackknife resampling of CP models. For the first occurrence, we describe the limitations of "mapping to building-blocks" when computing multiple, low-rank CP decompositions. After close collaboration with Chemometrics practitioners, we present a method (and algorithm), CP-CALS, which leverages information about their workflow, to overcome said limitations and achieve better performance. For the second occurrence, we describe the unique challenge of Jackknife resampling. We present a solution that addresses this challenge by making it possible to use CP-CALS to significantly increase performance, at the cost of slightly increasing the total amount of required computation. Through extensive experimentation with synthetic and real datasets on single-threaded and multi-threaded architectures, as well as on accelerators, we illustrate the improved efficiency and performance of our methods. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 7 Monate

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Einladung Informatik Oberseminar Christian Cherek

von Cherek, Christian

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 28. November 2022, 14:30 Uhr Ort: Raum 2222, Ahornstr. 55 Referent: Christian Cherek M.Sc. Lehrstuhl Informatik 10 Thema: The Impact of Tangible Interaction Techniques on Higher Cognitive Processes Abstract: Multitouch interaction brought incredible advancements to our everyday life. The success of smartphones is unprecedented in modern history for a good reason. On multitouch displays, input and output are collocated at the tip of our fingers. This enables immediate feedback, highly flexible utilization of the available space, updatability of interfaces, and new accessibility features. However, a touchscreen's flat surface lacks haptic features, neglecting a big part of our sensory capabilities. This thesis integrates itself into the tangible research community by presenting novel ways to create tangibles for capacitive screens and presenting a software framework to develop tangible applications with Apple's native APIs. We developed the Design Space of Tangible Interaction, a taxonomy to help researchers and designers comparing tangible designs and finding new ways to interact with tangibles. In this spirit, we evaluated tangibles in novel ways beyond their well-established usability benefits. We found them to contribute to users' way of thinking, awareness for collaborators, and intuitiveness of highly complex input tasks. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 8 Monate

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Einladung Informatik-Oberseminar Parnia Bahar, 24. November 2022,

von Stephanie Jansen

********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * *********************************************************************** Zeit: Donnerstag, 24. November 2022, 10:00 Uhr Zoom: https://rwth.zoom.us/j/93616983610?pwd=UnkwdWd4azRSNzVVUEt4WW1FdHNJUT09 Meeting-ID: 936 1698 3610 Kenncode: 382088 Referentin: Parnia Bahar, M.Sc. Thema: Neural Sequence-to-Sequence Modeling for Language and Speech Translation Abstract: In recent years, various fields in human language technology have been advanced by the success of neural sequence-to-sequence modeling. The application of attention models to automatic speech recognition, text, and speech machine translation has become dominant and well-established. Although the effectiveness of such models has been documented in scientific papers, not all aspects of attention sequence-to-sequence models have been explored. Therefore, the main contribution of this thesis centers around redesigning attention models by proposing novel alternative architectures. From a modeling perspective, this research goes beyond current sequence-to-sequence backbone models to directly incorporate input and output sequences in a two-dimensional structure where an attention mechanism is no longer required. This model distinguishes itself from attention models in which inputs and outputs are treated as one-dimensional sequences over time. Current state-of-the-art attention models also lack an explicit alignment, a core component of traditional systems. Such a gross simplification of a complex process complicates the extraction of alignments between input and output positions. To enable the explainability of attention models and more controllable output, the next part of this study integrates the attention model into the hidden Markov model formulation by introducing alignments as a sequence of hidden variables. Finally, an exciting research direction is combining speech recognition with text machine translation for speech-to-text translation. Besides advancing a cascade of independently trained speech recognition and machine translation systems, this thesis sheds light on different end-to-end models to directly translate speech into a target text and shows that such end-to-end models can practically translate speech utterances as a substitute solution to cascaded speech translation. 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 Theaterstraße 35-39 D-52062 Aachen Tel: +49 241 80-21601 sek(a)hltpr.rwth-aachen.de www.hltpr.rwth-aachen.de

1 Jahr, 8 Monate

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Einladung: Informatik-Oberseminar Herr Daxin Liu

von Maaßen, Leany

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Montag, 21.11.2022, 14:00-15:00 Uhr Der öffentliche Vortrag findet hybrid statt: Raum: Raum 5053.2 (B-IT-Hörsaal)/Informatikzentrum, Ahornstraße 55 Zoom: https://rwth.zoom.us/j/96565981989?pwd=b3B3aEVmSnJ1VFJhUDYwSlorbTcvQT09 Meeting-ID: 965 6598 1989 Kenncode: 346594 Referent: Herr Daxin Liu, M. Sc. LuFG Informatik 5 Thema: Projection in a Probabilistic Epistemic Logic and Its Application to Belief-based-Program Verification Abstract: Rich representation of knowledge and actions has been a goal that many AI researchers pursue. Among all proposals, perhaps, the situation calculus by Reiter is the most widely studied, where actions are treated as logical terms and the agent's knowledge is represented by logical formulas. The language has been extended to incorporate many features like time, concurrency, procedures, etc.. Most recently, Belle and Lakemeyer proposed a modal logic DS which deals with degrees of belief and noisy sensing. The logic has many appealing properties like full introspection, however, it also has some shortcomings. Perhaps the main one is the lack of expressiveness when it comes to degrees of belief. Currently, the language allows expressing degrees of belief only as constants making it impossible to express belief distribution. Another important problem is that it lacks projection reasoning mechanisms. Projection is the task to determine whether a query about the future is entailed by an initial knowledge base. Two solutions of projection exist regression and progression. While regression transfers the query about the future into a query about the initial state and evaluates it there, progression transfers the whole initial knowledge base into a future one. In this thesis, we first lift the expressiveness of the logic DS by modifying both the syntax and semantics. Moreover, we investigate the projection problem in DS. In particular, we propose a regression operator which can handle queries with nested beliefs and beliefs with quantifying-in. For progression, we show that classical progression is first-order definable for a fragment of the logic and provide our solution for the progression of belief in terms of only-believing after actions. Moreover, we exploit how to apply the proposed methods in a more practical scenario: on the verification of belief programs, a probabilistic extension of Golog programs, where every action and sensing could be noisy and every test refers to the agent's subjective beliefs. We show that the verification problem is undecidable even in very restrictive settings. We also show a special case where the problem is decidable. 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., 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

1 Jahr, 8 Monate

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[Reminder] Guest talk by Salil Kanhere next Monday

von Klaus Wehrle

Dear colleagues and students, as a reminder ... We invite you to join a guest talk by our visiting professor and Alexander- von-Humboldt-awardee Salil Kanhere of UNSW Sydney this afternoon. Best Regards klaus When? Monday, October 24, 15:30 Where? Room 9222, E3 building, Ahornstraße 55 The title of the talk will be: Practical and Extensible Decentralised Identity Management Abstract: Self-Sovereign Identity (SSI) is an emerging, user-centric, decentralized identity approach affording entities greater control over their identity and data flow during digital interactions. For digital credentials to be widely accepted, there is a need for an end-to-end system that provides secure verification of the participant identities and credentials to increase trust, and a data minimisation mechanism to reduce the risk of oversharing the credential data. In this talk, we first introduce CredChain, a blockchain-based SSI platform that allows secure creation, sharing and verification of credentials. Beyond the verification of identities and credentials, the self-sovereign identity architecture allows users to have full control over their credential data using a digital wallet, including the ability to selectively disclose part of credential data, as necessary. Current SSI solutions, assume the issuers to be “official” entities (e.g., government agencies) who must follow a stringent process to vet their credentials. However, there is no systematic support for directing the same level of trust agencies for individual users who may issue credentials (e.g., delegation of access, consent letter) in the context of business processes. A verifier who relies on user-issued credentials to complete a business process (e.g., a postal worker handing over parcel to someone other than the addressee) bears the risk of accepting these credentials without reliance on a trust agency. The second part of the talk presents CredTrust, a blockchain-based SSI framework that allows individual users to be “onboarded” to the platform as a verifiable issuer via the establishment of a "chain of trust". The talk will end with an overview of TradeChain, an architecture for decoupling identities and trade activities on blockchain enabled supply chains. TradeChain incorporates two separate ledgers: a public permissioned blockchain for maintaining identities and the permissioned blockchain for recording trade flows. Traders use Zero Knowledge Proofs (ZKPs) on their private credentials to prove multiple identities on the trade ledger. Traders can define dynamic access rules for verifying traceability information from the trade ledger using access tokens and Ciphertext Policy Attribute-Based Encryption (CP-ABE).

1 Jahr, 9 Monate

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