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

Einladung: Informatik-Oberseminar Tim Hartmann

von Tim Hartmann

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 6. Oktober 2022, 15:00 Uhr Ort: Raum 9222, Ahornstr. 55 Referent: Tim Hartmann M.Sc. Lehrstuhl Informatik 1 Thema: Facility Location on Graphs Abstract: We study two closely related Facility Location problems on graphs where all edges have unit length and where the facilities may also be positioned in the interior of the edges. For delta-Dispersion the goal is to position as many facilities as possible subject to the condition that any two facilities have at least distance delta from each other. For delta-Covering the goal is to cover the entire graph with the minimum number of facilities; that is, we want to position as few facilities as possible subject to the condition that every point on every edge is at distance at most delta from one of these facilities. We investigate the algorithmic complexity of these problems for every real distance value delta. Further, we explore the complexity of these problems with the solution size as parameter. Finally, we study delta-Dispersion with parameters that measure the complexity of the input graph, such as treewidth, treedepth and neighborhood diversity. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 7 Monate

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[Informatik-assistenten] Einladung: Informatik-Oberseminar Rebecca

von Rebecca Haehn

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 21. Oktober 2022, 14:30 Uhr Ort: Raum 9U10 (2359|0.10), E3, Informatikzentrum, Ahornstr. 55 Referent: Rebecca Haehn M.Sc. (Theory of Hybrid Systems) Thema: Optimisation and Analysis of Railway Timetables under Consideration of Uncertainties Abstract: Railway systems are complex systems that are strongly affected by uncertainties like weather, technical problems, or demand. Despite these uncertainties, railway systems need to function efficiently. In general this thesis aims to advance the consideration of uncertainty in the railway planning process to optimally utilise the existing railway network capacity. The focus in this thesis is on the delays that result from the uncertain environmental conditions. To consider these in the railway planning process, a symbolic simulation algorithm is proposed to examine the delay propagation in a railway network for a given timetable. This allows to estimate the timetable robustness and the network capacity. Several performance indicators for railway timetables that can be evaluated using the symbolic simulation are discussed. To optimally utilise the network capacity also an algorithm to schedule additional freight trains is presented. The main contributions of this thesis are the following: - An algorithm to schedule additional freight trains is presented, to utilise the remaining network capacity without disturbing an existing timetable. - A novel symbolic simulation algorithm for railway timetables is proposed. The algorithm receives as input a railway infrastructure model, a corresponding timetable, and discrete primary delay dis- tributions. It computes iteratively over time the delay propagation in the given railway system. Symbolic expressions are used to represent multiple possible values for the primary delays. This enables to simulate all discrete primary delay combinations at once. - An implementation of these algorithms is provided in C++ and evaluated on some real-world railway infrastructure networks and timetables based on the German railway system. The applicability and functionality of the algorithms is demonstrated. The proposed symbolic simulation algorithm is aimed to be a helpful addition to existing railway timetable simulations, which are mostly based on Monte Carlo simulation. In contrast to those, the symbolic approach stores the history of specific train states, which can be used to explain the occurring delays. In addition, the results of the symbolic simulation are exact with respect to the input model and the discrete primary delay distributions. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 7 Monate

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[sekretariate] Einladung: Informatik-Oberseminar Shahid Khan

von Elke Ohlenforst

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 4. October 2022, 10:00 Uhr Ort: Raum 9222, Geb. E3, 2. Etage, Informatikzentrum, Ahornstr. 55 Referent: Shahid Khan M.Sc. (Lehrstuhl Informatik 2) Thema: Boolean-logic Driven Markov Processes - Explained. Analysed. Verified. Abstract: Model-based dependability studies of engineering systems amount to quantifying and improving dependability measures. These measures include reliability, availability, maintainability and safety. Two key ingredients of such studies are 1) models capturing the system behaviour to an acceptable level of abstraction and 2) efficient and accurate analysis techniques to quantify dependability measures. Among the modelling techniques, static (or standard) fault trees (SFTs) is a prominent dependability modelling language extensively used by engineers to develop system models. However, it lacks expressive power as it cannot model temporal dependencies of failures and has limited support for repairs. Boolean logic-driven Markov processes (BDMPs) and dynamic fault trees (DFTs) are two classical dynamic extensions of SFTs that aim to mitigate the expressive power limitation issue of SFTs. While DFTs are (generally) restricted to non-repairable systems, BDMPs natively support repairs. The BDMP language has a long-standing industrial usage history; the leading French electricity utility company (EDF) extensively uses BDMPs to conduct dependability studies. Among the analysis techniques, probabilistic model checking is a verification technique to determine the probability of a state-space model satisfying or refuting a logical property. It combines efficient, fully automated verification algorithms with numerical analysis. The results of these automated verification procedures are numeric values that dependability practitioners use to attain the reliability growth of their systems. The issue with BDMPs is that it lacks numerically exact analysis support and adequately documented semantics. EDF provides two analysis tools for BDMPs: 1) a Monte-Carlo simulator and 2) a sequence exploration tool. Both tools provide approximate results, which may not be acceptable for stringent dependability requirements of safet-criticalsystems. The semantics of BDMPs is essential for a comparative study with other modelling languages, e.g., DFTs. This dissertation, developed in collaboration with EDF, presents semantics and a model checker for BDMPs: -- we propose Markov automaton- and generalised stochastic Petri net-based semantics to BDMPs and empirically establish the accuracy, -- we develop a probabilistic model checker for BDMPs and enhance the scalability of the model checker using partial state-space exploration techniques, and -- we contrast BDMPs to DFTs and lift the repairable behaviour of BDMPs to repairable DFTs. This dissertation provides a holistic view of BDMPs. An exciting outcome of this dissertation is that our model checker can analyse the Markovian subset of the EDF-maintained Figaro language. This subset includes capacity analysis diagrams, dynamic reliability block diagrams, electric circuits, Petri nets, process diagrams, telecommunication networks, and other EDF-developed Figaro-based modelling formalisms. In this presentation, we provide: (1) a detailed introduction to the BDMP formalism, (2) an insight of the BDMP model checker, and (3) the semantics of BDMPs. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 7 Monate

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Einladung: Informatik-Oberseminar Herr Thomas Osterland

von Leany Maaßen

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 20.09.2022, 10:00-11:00 Uhr Der öffentliche Vortrag findet hybrid statt: Raum: Raum 5053.2 (B-IT-Hörsaal)/Informatikzentrum, Ahornstraße 55 Zoom: https://rwth.zoom.us/j/91824143381?pwd=dU5SNSt2TWtUMlZTOVFtRkczN1RvQT09 Meeting-ID: 918 2414 3381 Kenncode: 827047 Referent: Herr Thomas Osterland, M. Sc. Lehrstuhl Informatik 5 Thema: Distributed Ledger Technology Processes Abstract: DLT offers new means to establish trust relationships within cooperative processes among enterprises and thereby enables the implementation of use cases with a lack of existing intermediaries. Prominent examples of use cases are financial applications, provenance tracking and identity management. All of them are contingent upon trust in business processes and require the maintained security of the distributed ledger, that strongly depends on the heterogeneity and robustness of the underlying ecosystem. Participants need to trust the security of the ecosystem and the trust must be maintained over time. Therefore, the distributed ledger technology is not a simple component, that can be easily integrated into an existing application unless the complex relationship between participants and their trust into the underlying ecosystem is considered early in the engineering process. This thesis introduces a framework, that comprises a structured methodology and tooling as a means to support conventional software processes that allow software architects to address the specific requirements of distributed ledger applications. We argue that these subjects are important to ensure the quality and sustainability of an application, since 1) a distributed ledger, that is not capable of handling future requirements of a use case represents eventually a bottleneck and thus, harms the efficient and reliable execution of business processes, 2) smart contracts with software bugs, that either prevent the efficient execution of business processes or favor one party over another will erode the trust that participants have into the ecosystem, and 3) if the DLT is not capable of securing substantial amounts of data use cases, that rely on the processing of data are disadvantaged and the consequences for an ecosystem when handling large amounts of data in the future are unforeseeable. 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

1 Jahr, 8 Monate

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Einladung: Informatik-Oberseminar Janosch Fuchs

von fuchs

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 26. August 2022, 13.00 Uhr Der öffentliche Vortrag findet hybrid statt: Ort: Raum 9222, Ahornstr. 55 Zoom: https://rwth.zoom.us/j/92249056004?pwd=cHkwRW1lNTlmS1RXQW95Wmw1MjhFQT09 Referent: Janosch Fuchs M.Sc. Lehrstuhl Informatik 1 Thema: Graph Exploration with Advice (and Online Crossing Minimization) Abstract: Online problems reveal their input instance piece by piece and require an irrevocable decision each time a new piece is revealed. This scenario models critical applications where a decision, once it is made, cannot be changed afterwards and also influences future decisions. Like for classical offline problems, it is common to make a worst-case analysis to give a guarantee on the performance of an online algorithm. A worst-case analysis is achieved by assuming that a malicious adversary, knowing the behavior of the online algorithm, creates the input. Due to the nature of uncertainty online algorithms rarely compute an optimal solution. Thus, their performance is measured in terms of the competitive ratio, which compares the solution computed by the online algorithm to the optimal solution achievable when the whole instance is known beforehand. To measure the impact of the missing knowledge about the future the classical online setting was extended with a helpful oracle providing information about the future input. The online algorithm receives the information by accessing a tape containing a binary bit string. The number of bits that the algorithm reads during its computation is called its advice complexity. Bounds on the advice complexity of an online algorithm that optimally solves an online problem show how much information about the future is needed to avoid any mistake. An obvious upper bound for the advice complexity of all online problems is the size of the binary encoding of the input instance or the optimal solution. But most of the time it is possible to derive better bounds by encoding only critical decisions which then reveals the core difficulty of an online problem. Thus, the advice complexity strongly correlates to the difficulty of an online problem and it can be used to measure the difficulty of different online problems. In the main part, we analyze the advice complexity of the graph exploration problem. Here, an algorithm has to move an agent through a graph from vertex to vertex using the edges such that the agent traverses the cheapest or shortest tour that visits every vertex at least once. The algorithm does not know the graph beforehand and each time the agent is located at a new vertex the reachable neighborhood is revealed together with the cost to reach them from the current location. Already seen or visited vertices are recognizable. Due to the already known tight upper bound of n\log(n) for the advice complexity of the graph exploration problem on dense graphs, we focus on sparse graphs. We show that a linear amount of advice is sufficient and also necessary to solve the graph exploration problem on directed and undirected graphs. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 9 Monate

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Einladung: Informatik-Oberseminar Herr Zhijian Li

von Leany Maaßen

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 10.08.2022, 14:00-15:00 Uhr Ort: Raum 5053.2 (großer B-IT-Raum)/Informatikzentrum, Ahornstraße 55 Referent: Herr Zhijian Li , M.Sc. Institute for Computational Genomics Thema: Computational Method for Single-cell ATAC-seq Imputation and Dimensionality Reduction Abstract: Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) allows mapping of regulatory variation of thousands of cells at the single-cell resolution. However, analyzing the scATAC-seq data is computationally challenging due to the sparsity, high dimensionality, and the nature of the binary signal. In this thesis, we proposed scOpen, a computational approach for quantification of single-cell open chromatin status and reduction of the dimensionality based on the non-negative matrix factorization (NMF) topic modeling. We demonstrated that scOpen can improve several crucial downstream analysis steps of scATAC-seq, such as clustering, visualization, cis-regulatory DNA interactions, and delineation of regulatory features. We also applied scOpen to investigate the regulatory programs that drive the development of chronic kidney disease (CKD). Altogether, these results demonstrate that scOpen is a useful computational approach in single-cell open chromatin data analysis. 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

1 Jahr, 9 Monate

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

von Tim Seppelt

Dear all, this is a reminder for Gerhard Lakemeyer's talk on Tractable Reasoning in First-Order Knowledge Bases <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. > In knowledge representation, obtaining a notion of belief which is > tractable, expressive, and eventually complete has been a somewhat > elusive goal. Expressivity here means that an agent should be able to > hold arbitrary beliefs in a very expressive language like that of > first-order logic, but without being required to perform full logical > reasoning on those beliefs. Eventual completeness means that any > logical consequence of what is believed will eventually come to be > believed, given enough reasoning effort. > > Tractability in a first-order setting has been a research topic for > many years, but in most cases limitations were needed on the form of > what was believed, and eventual completeness was so far restricted to > the propositional case. In this talk I present a novel logic of > limited belief, which has all three desired properties. 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 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, 10 Monate

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

von Tim Seppelt

Dear all, this is a reminder for Britta Peis' talk on Stackelberg Network Pricing Games <https://www.unravel.rwth-aachen.de/go/id/tbciy?lidx=1#aaaaaaaaaatbcjr> taking place *today at 16:30* in room 5053.2 and on Zoom. Please find the details below. > We study a variant of bilevel games, termed Stackelberg network > pricing games, in which one distinguished player, the leader, can set > prices on a subset of the edges or vertices in the underlying network. > The remaining edges or vertices are assigned a fixed costs. Based on > the leader’s decision, one or several followers optimize a > polynomial-time solvable combinatorial optimization problem. That is, > after the leader decided on the prices, each follower individually > selects an optimal solution (e.g. a shortest path, a max weight > matching, or a min cost spanning tree) based on the fixed costs and > the leader’s prices. The leader’s goal is to select the prices such > that the resulting revenue, which is determined by the sold items and > their prices, is maximised. > > Briest et al. and Balcan et al. independently showed that the maximum > revenue can be approximated to a factor of (roughly) log k, where k is > the number of priceable elements. In the lecture, we discuss > algorithms and complexity results for Stackelberg network pricing > games in general, and Stackelberg Max Closure and Stackelberg > Bipartite Min Weight Vertex Cover, in particular. > > Parts of the talk are based on joint work with Karsten Jungnitsch and > Marc Schröder 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: * 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, 10 Monate

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Einladung Informatik-Oberseminar Arvid Butting

von Butting, Arvid

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 12. Juli 2022, 9.00 Uhr Zoom: https://rwth.zoom.us/j/99072269440?pwd=bXNrOXlwSUhtd3IvVVlhNytURUY2UT09 Referent: Arvid Butting M.Sc. Lehrstuhl Informatik 3 Thema: Systematic Composition of Language Components in MontiCore Abstract: In model-driven development (MDD), models are central software engineering artifacts. MDD is applied to various domains such as avionics, law, mechanical engineering, or robotics, in which the domain engineers are not always software engineers. To this end, modelers should specify models in a notation close to the application domain, which is achieved by employing domain-specific modeling languages (DSMLs). In complex modern software applications, different aspects of an application are modeled with numerous integrated models. The models conform to heterogeneous, integrated DSMLs that can assure consistency between the models of an application. Ad-hoc development of DSMLs is a time-consuming and error-prone process. Systematic and "off-the-shelf" black-box reuse of DSMLs or parts of it supports engineering DSMLs faster and more reliably. In black-box reuse, unlike reuse via clone-and-own, the reused parts remain unchanged and do not result in co-existing clones. Such reuse requires language engineers to be able to integrate DSMLs through different forms of language composition. Current approaches for engineering DSMLs often rely on generic language infrastructure, which complicates compatibility checks between the infrastructures of languages that are to be composed. Approaches for modularization of DSMLs typically focus on the conceptual parts of a language rather than on their realizations. This talk describes an approach for realizing modular language components that can be composed via their symbol tables to realize language product lines with the language workbench MontiCore. The proposed language components identify the entirety of source code artifacts that realize a DSML. The DSMLs rely on kind-typed symbol tables that assure language compatibility during language composition. Language composition via symbol tables is lightweight because language infrastructures are only loosely coupled. An approach for persisting symbol tables further decouples language infrastructures from another and increases the performance for type and consistency checking between models that conform to different DSMLs. With the approach for language product lines, language components can be composed systematically and undesired compositions can be avoided. Typed and persisted symbol tables, language components, and language product lines as presented in this talk aim to realize DSML engineering in the large. Es laden ein: die Dozentinnen und Dozenten der Informatik

1 Jahr, 10 Monate

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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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