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

Neuer Zoom-Link I WG: Einladung Promotionsverteidigung Rezaul Karim,

von Romina Reddig

Sehr geehrte Damen und Herren, leider hat sich ein Fehler im Zoom-Link eingeschlichen. Bitte beachten Sie deshalb die nachfolgenden Informationen sowie den entsprechenden Link. Vielen Dank! +********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 28.06.2022, 10:00-11:00 Uhr Der öffentliche Vortrag findet hybrid statt: Raum: Raum 5053.1 (kleiner B-IT-Raum)/Informatikzentrum, Ahornstraße 55 Zoom: https://zoom.us/j/91338931025?pwd=SDN3TWh2c0ZTb2xjWGkwdHZYLzQ5QT09 Referent: Herr Md Rezaul Karim, Master of Engineering Lehrstuhl Informatik 5 Thema: Interpreting Black-Box Machine Learning Models with Decision Rules and Knowledge Graph Reasoning Abstract: Machine learning (ML) algorithms are increasingly used to solve complex problems. However, due to high non-linear and higher-order interactions between features, complex ML models become black-box methods - which means it is not known how certain predictions are made. This may not be acceptable in many situations (e.g., in clinical situations where AI may significantly impact human lives). With the EU GDPR explainability has not only become a desirable property of AI but also a legal requirement. An interpretable ML model can outline how input instances are mapped into certain outputs by identifying statistically significant features. Literature pointed out that complex ML models tend to be less interpretable, showing a trade-off between accuracy and interpretability. This thesis aims to improve the interpretability and explainability of black-box ML models without sacrificing significant predictive accuracy. As a starting point, using a black-box multimodal neural network, representation learning is performed on multimodal data in order to use the learned representation for the classification task. To improve the interpretability of the learned black-box model, different interpretable ML methods such as probing, perturbing, and model surrogation techniques are applied. An interpretable surrogate model is trained to approximate the behavior of the back-box model. The surrogate model is used to generate explanations in terms of decision rules and counterfactuals. To add symbolic reasoning capability to the black-box model, a domain-specific knowledge graph (KG) is constructed by integrating knowledge and facts from scientific literature. A semantic reasoner is then used to validate the association of significant features with different classes based on relations it learned from the KG. Evidence-based decision rules are generated by combining the reasoning with the predictions from the black-box model. The quantitative evaluation shows that the proposed approach achieves an average accuracy of 96.25% on the test dataset. It can also provide human-interpretable explanations of the decisions in the form of counterfactual rules and evidence-based decision rules. The quality of the explanations is evaluated in terms of comprehensiveness and sufficiency. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Romina Reddig _______________________________ Romina Reddig 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: 0241-80-21501 Fax: 0241-80-22321 E-Mail: reddig(a)dbis.rwth-aachen.de <mailto:reddig@dbis.rwth-aachen.de> Von: Romina Reddig <reddig(a)dbis.rwth-aachen.de> Gesendet: Montag, 20. Juni 2022 11:41 An: assistenten(a)informatik.rwth-aachen.de; infprof(a)informatik.rwth-aachen.de; vortraege(a)informatik.rwth-aachen.de; sekretariate(a)informatik.rwth-aachen.de; webteam(a)informatik.rwth-aachen.de; biblio(a)informatik.rwth-aachen.de Betreff: Einladung Promotionsverteidigung Rezaul Karim, 28. Juni 2022, 10:00 Uhr +********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 28.06.2022, 10:00-11:00 Uhr Der öffentliche Vortrag findet hybrid statt: Raum: Raum 5053.1 (kleiner B-IT-Raum)/Informatikzentrum, Ahornstraße 55 Zoom: https://zoom.us/j/94726175294?pwd=QXJVeGVvZ09wV000OU53QkdrU0RRdz09 Referent: Herr Md Rezaul Karim, Master of Engineering Lehrstuhl Informatik 5 Thema: Interpreting Black-Box Machine Learning Models with Decision Rules and Knowledge Graph Reasoning Abstract: Machine learning (ML) algorithms are increasingly used to solve complex problems. However, due to high non-linear and higher-order interactions between features, complex ML models become black-box methods - which means it is not known how certain predictions are made. This may not be acceptable in many situations (e.g., in clinical situations where AI may significantly impact human lives). With the EU GDPR explainability has not only become a desirable property of AI but also a legal requirement. An interpretable ML model can outline how input instances are mapped into certain outputs by identifying statistically significant features. Literature pointed out that complex ML models tend to be less interpretable, showing a trade-off between accuracy and interpretability. This thesis aims to improve the interpretability and explainability of black-box ML models without sacrificing significant predictive accuracy. As a starting point, using a black-box multimodal neural network, representation learning is performed on multimodal data in order to use the learned representation for the classification task. To improve the interpretability of the learned black-box model, different interpretable ML methods such as probing, perturbing, and model surrogation techniques are applied. An interpretable surrogate model is trained to approximate the behavior of the back-box model. The surrogate model is used to generate explanations in terms of decision rules and counterfactuals. To add symbolic reasoning capability to the black-box model, a domain-specific knowledge graph (KG) is constructed by integrating knowledge and facts from scientific literature. A semantic reasoner is then used to validate the association of significant features with different classes based on relations it learned from the KG. Evidence-based decision rules are generated by combining the reasoning with the predictions from the black-box model. The quantitative evaluation shows that the proposed approach achieves an average accuracy of 96.25% on the test dataset. It can also provide human-interpretable explanations of the decisions in the form of counterfactual rules and evidence-based decision rules. The quality of the explanations is evaluated in terms of comprehensiveness and sufficiency. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Romina Reddig _______________________________ Romina Reddig 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: 0241-80-21501 Fax: 0241-80-22321 E-Mail: reddig(a)dbis.rwth-aachen.de <mailto:reddig@dbis.rwth-aachen.de>

1 Jahr, 10 Monate

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Einladung: Informatik-Oberseminar Jens Hiller

von Jens Hiller

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 1. Juli 2022, 9:00 Uhr Ort: Seminarraum Informatik 4 (COMSYS), E3, Ahornstr. 55 Zoom (hybrider Vortrag): https://rwth.zoom.us/j/91545576628?pwd=d2JoNjVMKysvNU9lWWhzd200R3hJdz09 Referent: Jens Hiller M.Sc. Lehrstuhl Informatik 4 (COMSYS) Thema: Improving Functionality, Efficiency, and Trustworthiness of Secure Communication on an Internet diversified by Mobile Devices and the Internet of Things Abstract: Secure communication is essential for many use cases that exchange data over the Internet. However, prevalently used security protocols, e.g., TLS 1.2, have been standardized many years ago. At that time, the Internet was dominated by traditional devices and communication scenarios, especially location-bound workstations communicating with servers or cloud services. Since then, the advent of smartphones and the Internet of Things (IoT) introduced new scenarios with more diverse device types and use cases. The development to this evolved Internet motivates the question for a likewise evolved secure communication that fits new demands. To examine the need for improvements, we analyze the state of the art of secure communication for the different scenarios of the evolved Internet. Our analysis reveals several open challenges, especially missing advanced security and privacy features for secure communication in the IoT, the need for increased efficiency of secure communication by smartphones, and the demand for efficient secure low-latency communication in the industrial IoT. Furthermore, also considering the traditional Internet, we identify open problems in the PKI-based trust infrastructure, and highlight the need to understand drivers and obstacles of the roll-out of new security mechanisms to improve their adoption and effective use. We tackle these open challenges with four contributions. First, we tailor the Tor anonymity network to resource-constrained IoT devices to protect the metadata of IoT communication and also realize a resource-efficient in-network access control. Additionally, we enable IoT devices to use large and versatile secure communication stacks. Second, we increase secure communication efficiency by realizing secure low-latency communication for the industrial IoT. Moreover, we devise more efficient best practices for the establishment of secure connections by smartphones. Third, focusing on the trust infrastructure of secure communication, we provide a detailed risk analysis of cross-signing in the Web PKI, revealing that it can cause undesired certificate trust paths. Fourth, we analyze drivers and obstacles for the effective roll-out of adapted security protocols and procedures based on measurements of TLS 1.3 and Certification Authority Authorization. Overall, we show the need for adapting secure communication to the evolved Internet and present corresponding improvements. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 10 Monate

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Einladung Informatik Kolloquium Hector Geffner / Invitation Computer

von van der Aalst, Willibrordus

*********************************************************************** * * Informatik-Kolloquium / Computer Science Colloquium * *********************************************************************** Presenter: Hector Geffner (ICREA and Universitat Pompeu Fabra, Barcelona, Spain & Linköping University, Sweden) Title: Language-based representation learning for acting and planning Time: Monday 27-6-2022, 14.30 Location: Room 9222, E3 (Informatik-Zentrum, Erweiterungsbau E3, https://www.informatik.rwth-aachen.de/cms/informatik/Fachgruppe/Informatik-…) Abstract: Recent breakthroughs in AI have shown the remarkable power of deep learning and deep reinforcement learning. These developments, however, have been tied to specific tasks, and progress in out-of-distribution generalization has been limited. While it is assumed that these limitations can be overcome by incorporating suitable inductive biases in neural nets, this is left vague and informal, and does not provide meaningful guidance. In this talk, I articulate a different learning approach where representations are learned over domain-independent target languages whose structure and semantics yield a meaningful and strongly biased hypothesis space. The learned representations do not emerge from biases in a low level architecture but from a general preference for the simplest hypothesis that explain the data. I illustrate this general idea by considering three concrete learning problems in AI planning: learning general actions models, learning general policies, and learning general subgoal structures ("intrinsic rewards"). In all these cases, learning is formulated and solved as a combinatorial optimization problem although nothing prevents the use of deep learning techniques instead. Indeed, learning representations over domain-independent languages with a known structure and semantics provides an account of what is to be learned, while learning representations with neural nets provides a complementary account of how representations can be learned. The challenge and the opportunity is to bring the two approaches together. Reference: Target languages (vs. inductive biases) for learning to act and plan. Hector Geffner. AAAI 2022. https://arxiv.org/abs/2109.07195 Short Bio: Hector Geffner is an ICREA Research Professor at the Universitat Pompeu Fabra (UPF) in Barcelona, Spain, and a Wallenberg Guest Professor at Linköping University. He grew up in Buenos Aires and obtained a PhD in Computer Science at UCLA in 1989. He then worked at the IBM T.J. Watson Research Center in NY, USA, and at the Universidad Simon Bolivar, in Caracas. Hector teaches courses on logic, AI, and social and technological change, and is currently doing research on representations learning for acting and planning as part of the ERC project RLeap 2020-2025. Es laden ein: die Dozentinnen und Dozenten der Informatik <http://www.vdaalst.com/> prof.dr.ir. Wil van der Aalst ● <http://www.vdaalst.com/> www.vdaalst.com ● @wvdaalst

1 Jahr, 11 Monate

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TODAY UnRAVeL Survey Lecture "What's New in UnRAVeL?"

von Tim Seppelt

Dear all, this is a reminder for Sebastian Trimpe's talk on Uncertainty Bounds for Gaussian Process Regression with Applications to Safe Control and Learning <https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Das-Graduiertenkolleg/Aktuel…>taking place *today at 16:30* in room 5053.2 and on Zoom. Please find the details below. > Gaussian Process (GP) regression is a popular nonparametric machine > learning method that provides uncertainty estimates for its > predictions. While GPs are based on Bayesian principles, also > frequentist uncertainty bounds are available, which are required for > applications in learning-based control or safe reinforcement > learning. However, the available uncertainty bounds are typically too > conservative to be useful in applications. This often leads > practitioners to replacing these bounds by heuristics, thus breaking > all theoretical guarantees. To address this problem, we introduce new > GP uncertainty bounds that are rigorous, yet practically useful at the > same time. In particular, the bounds can be explicitly evaluated and > are much less conservative than state of the art results. After an > introduction to Gaussian processes, we will discuss these results, and > present applications to learning-based control and safe reinforcement > learning. 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. The main aim is to provide doctoral researchers as well as master students a broad overview of the subjects of UnRAVeL. Science undergoes continuous change and lives from the constant quest for novel and better results, which are presented at conferences and in journals. This year, 10 UnRAVeL professors will present some of their most recent research successes. Everyone interested, in particular doctoral researchers and master students, are invited to attend the UnRAVeL lecture series 2022 and engage in discussions with the researchers. The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the ground floor of building E2. All events are hybrid. To join remotely, please use https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09 / Meeting ID: 960 0388 5007 / Passcode: 273710 Please find a list of all upcoming talks on the UnRAVeL website <https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…> and below: * 21/06/2022 Sebastian Trimpe: Uncertainty Bounds for Gaussian Process Regression with Applications to Safe Control and Learning * 28/06/2022 Britta Peis: Stackelberg Network Pricing Games * 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order Knowledge Bases We are looking forward to seeing many of you in the UnRAVeL survey lecture "What's New in UnRAVeL?". Best regards, Andreas Klinger, Birgit Willms, and Tim Seppelt Logo

1 Jahr, 11 Monate

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Einladung Promotionsverteidigung Rezaul Karim, 28. Juni 2022, 10:00 Uhr

von Romina Reddig

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 28.06.2022, 10:00-11:00 Uhr Der öffentliche Vortrag findet hybrid statt: Raum: Raum 5053.1 (kleiner B-IT-Raum)/Informatikzentrum, Ahornstraße 55 Zoom: https://zoom.us/j/94726175294?pwd=QXJVeGVvZ09wV000OU53QkdrU0RRdz09 Referent: Herr Md Rezaul Karim, Master of Engineering Lehrstuhl Informatik 5 Thema: Interpreting Black-Box Machine Learning Models with Decision Rules and Knowledge Graph Reasoning Abstract: Machine learning (ML) algorithms are increasingly used to solve complex problems. However, due to high non-linear and higher-order interactions between features, complex ML models become black-box methods - which means it is not known how certain predictions are made. This may not be acceptable in many situations (e.g., in clinical situations where AI may significantly impact human lives). With the EU GDPR explainability has not only become a desirable property of AI but also a legal requirement. An interpretable ML model can outline how input instances are mapped into certain outputs by identifying statistically significant features. Literature pointed out that complex ML models tend to be less interpretable, showing a trade-off between accuracy and interpretability. This thesis aims to improve the interpretability and explainability of black-box ML models without sacrificing significant predictive accuracy. As a starting point, using a black-box multimodal neural network, representation learning is performed on multimodal data in order to use the learned representation for the classification task. To improve the interpretability of the learned black-box model, different interpretable ML methods such as probing, perturbing, and model surrogation techniques are applied. An interpretable surrogate model is trained to approximate the behavior of the back-box model. The surrogate model is used to generate explanations in terms of decision rules and counterfactuals. To add symbolic reasoning capability to the black-box model, a domain-specific knowledge graph (KG) is constructed by integrating knowledge and facts from scientific literature. A semantic reasoner is then used to validate the association of significant features with different classes based on relations it learned from the KG. Evidence-based decision rules are generated by combining the reasoning with the predictions from the black-box model. The quantitative evaluation shows that the proposed approach achieves an average accuracy of 96.25% on the test dataset. It can also provide human-interpretable explanations of the decisions in the form of counterfactual rules and evidence-based decision rules. The quality of the explanations is evaluated in terms of comprehensiveness and sufficiency. Es laden ein: die Dozentinnen und Dozenten der Informatik -- Romina Reddig _______________________________ Romina Reddig 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: 0241-80-21501 Fax: 0241-80-22321 E-Mail: reddig(a)dbis.rwth-aachen.de <mailto:reddig@dbis.rwth-aachen.de>

1 Jahr, 11 Monate

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TODAY UnRAVeL Survey Lecture "What's New in UnRAVeL?"

von Tim Seppelt

Dear all, this is a reminder for Christina Büsing's talk A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty <https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Das-Graduiertenkolleg/Aktuel…> taking place *today at 16:30* in room 5053.2 and on Zoom. Please find the details below. > Since its introduction in the early 2000s, robust optimization with > budget uncertainty has received a lot of attention. This is due to the > intuitive construction of the uncertainty sets and the existence of a > compact robust reformulation for (mixed-integer) linear programs. > > However, despite its compactness, the reformulation performs poorly > when solving robust integer problems due to its weak linear relaxation. > > To overcome the problems arising from the weak formulation, we propose > a bilinear formulation for robust binary programming, which is as > strong as theoretically possible. From this bilinear formulation, we > derive strong linear formulations as well as structural properties for > robust binary optimization problems, which we use within a tailored > branch & bound algorithm. > > We test our algorithm’s performance together with other approaches > from the literature on a diverse set of “robustified” real-world > instances from the MIPLIB 2017. Our computational study, which is the > first to compare many sophisticated approaches on a broad set of > instances, shows that our algorithm outperforms existing approaches by > far. Furthermore, we show that the fundamental structural properties > proven in this paper can be used to substantially improve the > approaches from the literature. > > This highlights the relevance of our findings, not only for the tested > algorithms but also for future research on robust optimization. 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. The main aim is to provide doctoral researchers as well as master students a broad overview of the subjects of UnRAVeL. Science undergoes continuous change and lives from the constant quest for novel and better results, which are presented at conferences and in journals. This year, 10 UnRAVeL professors will present some of their most recent research successes. Everyone interested, in particular doctoral researchers and master students, are invited to attend the UnRAVeL lecture series 2022 and engage in discussions with the researchers. The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the ground floor of building E2. All events are hybrid. To join remotely, please use https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09 / Meeting ID: 960 0388 5007 / Passcode: 273710 Please find a list of all upcoming talks on the UnRAVeL website <https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…> and below: * 21/06/2022 Sebastian Trimpe: Uncertainty Bounds for Gaussian Process Regression with Applications to Safe Control and Learning * 28/06/2022 Britta Peis: Stackelberg Network Pricing Games * 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order Knowledge Bases We are looking forward to seeing many of you in the UnRAVeL survey lecture "What's New in UnRAVeL?". Best regards, Andreas Klinger, Birgit Willms, and Tim Seppelt Logo

1 Jahr, 11 Monate

1
0
0 0

Einladung: Informatik-Oberseminar Matthias Markthaler

von Markthaler, Matthias

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 22. Juni 2022, 15.00 Uhr Ort: Raum 9U10, Ahornstr. 55, Gebäude Informatik E3 [2359] - Untergeschoss Referent: Matthias Markthaler M.Sc. Lehrstuhl Informatik 3, Software Engineering Thema: Modellbasierte Methode für automatisierte Testfallerstellung in der Automobilindustrie auf der Grundlage eines durchgängigen Systems Engineering Ansatzes Kurzfassung (Abstract in English see below): Die heutige und zukünftige Komplexität in großen Cyber-Physischen Systemen ist ohne systematische und digitalisierte Herangehensweisen kaum noch beherrschbar. Ein aktuelles Paradebeispiel solcher Cyber-Physischen Systeme sind die Produkte der Automobilindustrie, die einer noch nie da gewesenen Komplexität gegenüberstehen. Die Komplexität in der Automobilindustrie steigt stetig mit den Kundenansprüchen wie Individualität, Nachhaltigkeit, autonomes Fahren und Sicherheit bei hohen Qualitätsansprüchen zu günstigen Preisen. In der Informatik konnte in den letzten Jahrzehnten die Komplexität, Qualität und Effizienz mit agilen Methoden und einer durchgängigen Verwendung von Modellen verbessert werden. Während agile Methoden in der Automobilindustrie bereits ein fester Begriff ist, steht eine durchgängige Verwendung von Modellen in diesem Bereich noch aus. Auch wenn eine Reihe erfolgversprechende modellbasierter Ansätze aufgesetzt wurden, konnte sich eine modellbasierte Entwicklung noch nicht vollständig etablieren. Im Rahmen dieses Vortrages wird eine durchgängige Entwicklungsmethode unter Verwendung von Modellen in Verbindung mit einem kompatiblen Testfallgenerator in der Industrieanwendung vorgestellt. Die Methode ist eine Weiterentwicklung von der modellbasierten Spezifikationsmethode für Anforderungen, Design und Test und erlaubt eine modellgetriebene Entwicklung für die automatisierte Erstellung von Artefakten wie beispielsweise Testfälle. Die wichtigsten Ergebnisse dieser Arbeit lassen sich wie folgt zusammenfassen: - Eine modellgetriebene Spezifikationsmethode für Anforderungen, Design und Test in einer Automobilindustrie-Anwendung. - Eine Statusanalyse des modellbasierten Testens in der Automobilindustrie. - Kriterien für die Testbarkeit der spezifizierten Modelle und für den Nutzen der erstellten Testfälle. - Eine domänenspezifische Sprache auf der Basis einer entwickelten MontiCoreGrammatik, die eine Maschinenlesbarkeit der Modelle gewährleistet. - Ein Testfallgenerator, der diese domänenspezifischen abstrakten Modelle für die automatisierte Erstellung von Testfällen nutzt. - Gewonnene aufgearbeitete Erkenntnisse aus der angewendeten Forschung in der Industrie. Die Erkenntnisse aus der Anwendung der Methoden und der Tools in der Automobilindustrie dienen als Leitlinien für die Übertragung weiterer Kenntnisse aus der Informatik in die Praxis von traditionellen maschinenbau-geprägten Industrien. Die vorgestellte modellbasierte Spezifikationsmethode wurde fest in der Entwicklung von elektrischen Antrieben in einem Automobilkonzern verankert. Die Spezifikationsmodelle dienen den testfallerstellenden Personen als Basis zur manuellen und automatisierten Absicherung der Systeme und ermöglichen eine breite, tiefe und infolgedessen qualitativ hohe Absicherung. Es laden ein: die Dozentinnen und Dozenten der Informatik --- Abstract : The current and future interdisciplinary complexity in large cyber-physical systems can hardly be managed without systematic and digitized approaches. A popular example of cyber-physical systems are automobiles, which are facing unprecedented complexity. The complexity in automobiles is constantly increasing with its customer demands such as individuality, sustainability, autonomous driving and safety at high-quality standards at reasonable prices. In software engineering, complexity, quality, and efficiency have been improved over the last decades with agile methods and consistent use of models. While agile methods are already established in the automotive industry, the consistent use of models is still pending. Even though a number of promising model-based approaches have been set up, model-based development has not yet fully been established. This dissertation presents a consistent model-driven development method and a compatible test case generator. The method is a further development of the model-based Specification Method for Requirements, Design, and Test and allows model-driven development for the automated creation of artifacts such as test cases. The main contributions of this thesis are: - A model-driven Specification Method for Requirements, Design, and Test used in the automotive industry, - A status analysis of model-based testing in the automotive industry, - A criteria catalog for the testability of the specified models and the usability of created test cases, - A domain-specific language based on a developed MontiCore grammar, which provides machine readability of models, - A test case generator that creates automated test cases out of these domain-specific abstract models, and Lessons learned from the applied research in the industry. The lessons learned serve as guidelines for the transfer of computer science methods to traditional mechanical engineering industries. The presented model-based specification method was firmly established in the electric drive development of a multinational automotive company. The specification models provide a basis for manual and automated testing and enable broad, deep, and high-quality safeguarding.

1 Jahr, 11 Monate

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Einladung: Informatik-Oberseminar Krishna Subramanian

von Subramanian, Krishna

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 17. Juni 2022, 14.00 Uhr Zoom: https://rwth.zoom.us/j/96718725247 Referent: Krishna Subramanian, M.Sc. Lehrstuhl Informatik 10 Thema: Lowering the Barriers to Hypothesis-Driven Data Science Abstract: Data science is a frequent task in academia and industry. One common use of data science is to validate hypotheses, in which the analyst uses significance-based hypothesis testing to draw insights about a population distribution based on experimental data. Apart from data scientists, who are professionally trained in data science and are highly skilled, many non-professional analysts also carry out data analysis. These non-professionals, who we refer to as data workers, are domain experts who lack expertise in data science, such as academic researchers, project managers, and sales managers. Through interviews, observations, online surveys, and content analyses, we aim to understand data workers' workflows across important tasks in hypothesis testing: learning theoretical and practical statistics, selecting statistical procedures, using data science programming IDEs to experiment with ideas in source code, refine and refactor source code, and disseminating findings from an analysis.  We present our findings grouped into two steps when performing data science tasks: 1. Preparing to perform data science tasks: We discuss our findings about the impact of formal training on real-world statistical practice; trade-offs among information sources used for selecting statistical procedures; perceived complexity and uncertainty about statistical procedure selection; and reluctance among data workers to adopt alternative methods of analysis.  Based on the above findings, we present design recommendations and two artifacts to improve data workers' workflows. Our artifacts include Statsplorer, a web-based tool to help data workers kickstart analysis and learn about common issues in statistical practice, such as over-testing, overlooking assumptions, and selecting the appropriate test; and StatPlayground, an interactive simulation tool that can be used to self-learn or teach statistical concepts and statistical procedure selection.  2. Performing data science tasks: Our findings include an overview of data workers' workflows when performing hypothesis testing using programming IDEs, which follows an exploratory programming workflow; and a comparison of existing interfaces for data science programming, namely computational notebooks, scripts, and consoles, and a discussion of how well they support various steps in hypothesis testing.  To improve data workers' workflows when performing data science tasks, we contribute design recommendations and two artifacts. Our artifacts include StatWire, an experimental hybrid-programming interface that encourages data workers to write high-quality source code; and Tractus, an interactive visualization that can lower the cost of working with experimental source code. Based on our work, we present four takeaways that can be used by researchers, software developers, and educators to lower the barriers to hypothesis testing. --- Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 11 Monate

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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

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TODAY UnRAVeL Survey Lecture "What's New in UnRAVeL?"

von Tim Seppelt

Dear all, this is a reminder for Erika Ábrahám's talk on The Challenge of Compositionality for Stochastic Hybrid Systems <https://www.unravel.rwth-aachen.de/go/id/taysw?lidx=1#aaaaaaaaaatayto> taking place *today at 16:30* in room 5053.2 and on Zoom. Please find the details below. > Hybrid systems are systems, whose behavior is composed from continuous > evolution interrupted by discrete state changes. Hybrid automata are > one of the most well-known formalisms to specify hybrid systems. > > Also different extensions of hybrid automata have been proposed in the > literature to model uncertainties. In this talk we will focus on > modeling stochasticity regarding the time point of discrete steps in a > compositional framework. The main aim of this talk is not to present a > modeling language, but rather to discuss the challenges that come with > the design of a compositional modeling language for stochastic hybrid > systems. 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. The main aim is to provide doctoral researchers as well as master students a broad overview of the subjects of UnRAVeL. Science undergoes continuous change and lives from the constant quest for novel and better results, which are presented at conferences and in journals. This year, 10 UnRAVeL professors will present some of their most recent research successes. Everyone interested, in particular doctoral researchers and master students, are invited to attend the UnRAVeL lecture series 2022 and engage in discussions with the researchers. The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the ground floor of building E2. All events are hybrid. To join remotely, please use https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09 / Meeting ID: 960 0388 5007 / Passcode: 273710 Please find a list of all upcoming talks on the UnRAVeL website <https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…> and below: * 24/05/2022 Erika Ábrahám: The Challenge of Compositionality for Stochastic Hybrid Systems * 21/06/2022 Sebastian Trimpe: Uncertainty Bounds for Gaussian Process Regression with Applications to Safe Control and Learning * 28/06/2022 Britta Peis: Stackelberg Network Pricing Games * 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order Knowledge Bases We are looking forward to seeing many of you in the UnRAVeL survey lecture "What's New in UnRAVeL?". Best regards, Andreas Klinger, Birgit Willms, and Tim Seppelt Logo

1 Jahr, 12 Monate

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