TY - GEN AB - Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control - potentially with non-smoothness both on the level of the objectives or in the system dynamics. This results in new challenges such as dealing with expensive models (e.g., governed by partial differential equations (PDEs)) and developing dedicated algorithms handling the non-smoothness. Since in contrast to single-objective optimization, the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging, which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview of recent developments in the field of multiobjective optimization of non-smooth PDE-constrained problems. In particular we report on the advances achieved within Project 2 "Multiobjective Optimization of Non-Smooth PDE-Constrained Problems - Switches, State Constraints and Model Order Reduction" of the DFG Priority Programm 1962 "Non-smooth and Complementarity-based Distributed Parameter Systems: Simulation and Hierarchical Optimization". AU - Bernreuther, Marco AU - Dellnitz, Michael AU - Gebken, Bennet AU - Müller, Georg AU - Peitz, Sebastian AU - Sonntag, Konstantin AU - Volkwein, Stefan ID - 46578 T2 - arXiv:2308.01113 TI - Multiobjective Optimization of Non-Smooth PDE-Constrained Problems ER - TY - CONF AU - Rautenberg, Frederik AU - Kuhlmann, Michael AU - Ebbers, Janek AU - Wiechmann, Jana AU - Seebauer, Fritz AU - Wagner, Petra AU - Haeb-Umbach, Reinhold ID - 44849 T2 - Fortschritte der Akustik - DAGA 2023 TI - Speech Disentanglement for Analysis and Modification of Acoustic and Perceptual Speaker Characteristics ER - TY - CONF AB - Many Android applications collect data from users. When they do, they must protect this collected data according to the current legal frameworks. Such data protection has become even more important since the European Union rolled out the General Data Protection Regulation (GDPR). App developers have limited tool support to reason about data protection throughout their app development process. Although many Android applications state a privacy policy, privacy policy compliance checks are currently manual, expensive, and prone to error. One of the major challenges in privacy audits is the significant gap between legal privacy statements (in English text) and technical measures that Android apps use to protect their user's privacy. In this thesis, we will explore to what extent we can use static analysis to answer important questions regarding data protection. Our main goal is to design a tool based approach that aids app developers and auditors in ensuring data protection in Android applications, based on automated static program analysis. AU - Khedkar, Mugdha ID - 44146 KW - static analysis KW - data protection and privacy KW - GDPR compliance T2 - Proceedings of the 45th International Conference on Software Engineering: Companion Proceedings (ICSE ‘23) TI - Static Analysis for Android GDPR Compliance Assurance ER - TY - GEN AU - Beckendorf, Björn ID - 52317 TI - Self-Stabilizing Skip-Graph with Growth-bounded Metric ER - TY - JOUR AU - Demir, Caglar AU - Wiebesiek, Michel AU - Lu, Renzhong AU - Ngonga Ngomo, Axel-Cyrille AU - Heindorf, Stefan ID - 46248 JF - ECML PKDD TI - LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals ER - TY - CONF AB - Megatrends, such as digitization or sustainability, are confronting the product management of manufacturing companies with a variety of challenges regarding the design of future products, but also the management of the actual products. To successfully position their products in the market, product managers need to gather and analyze comprehensive information about customers, developments in the products’ environment, product usage, and more. The digitization of all aspects of life is making data on these topics increasingly available – via social media, documents, or the internet of things from the products themselves. The systematic collection and analysis of these data enable the exploitation of new potentials for the adaption of existing products and the creation of the products of tomorrow. However, there are still no insights into the main concepts and cause-effect relationships in exploiting data-driven approaches for product management. Therefore, this paper aims to identify the main concepts and advantages of data-driven product management. To answer the corresponding research questions a comprehensive systematic literature review is conducted. From its results, a detailed description of the main concepts of data-driven product management is derived. Furthermore, a taxonomy for the advantages of data-driven product management is presented. The main concepts and the taxonomy allow for a deeper understanding of the topic while highlighting necessary future actions and research needs. AU - Fichtler, Timm AU - Grigoryan, Khoren AU - Koldewey, Christian AU - Dumitrescu, Roman ID - 52369 KW - Product Lifecyle Management (PLM) KW - Data Analytics KW - Data-driven Design KW - Engineering Management KW - Lifecycle Data T2 - 2023 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) TI - Towards a Data-Driven Product Management – Concepts, Advantages, and Future Research ER - TY - GEN AU - Vernholz, Mats ED - Perla, Loredana ED - Agrati, Laura Sara ED - Vinci, Viviana ED - Scarinci, Alessia ID - 52370 SN - 9791255681038 T2 - Living and Leading in the Next Era: Connecting Teaching, Research, Citizenship and Equity TI - Academic self-concepts of pre-service technology teachers for vocational education in Germany according to the TPACK- Model ER - TY - GEN AU - Knorr, Lukas AU - Schlosser, Florian AU - Meschede, Henning ID - 47540 TI - Economic Integration of a High-Temperature Heat Pump with Flexible Part-load Operation ER - TY - GEN AU - Klassen, Alexander ID - 52480 TI - Fast Partial Reconfiguration for ReconOS64 on Xilinx MPSoC Devices ER - TY - CONF AU - Prager, Raphael Patrick AU - Trautmann, Heike ED - Silva, Sara ED - Paquete, Luís ID - 52530 T2 - Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023 TI - Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems ER - TY - CONF AB - Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin\textendash Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100000 TSP instances with 2000 nodes each \textemdash currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets. AU - Seiler, Moritz Vinzent AU - Rook, Jeroen AU - Heins, Jonathan AU - Preuß, Oliver Ludger AU - Bossek, Jakob AU - Trautmann, Heike ID - 48898 T2 - 2023 IEEE Symposium Series on Computational Intelligence (SSCI) TI - Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP ER - TY - CHAP AB - Static analysis tools support developers in detecting potential coding issues, such as bugs or vulnerabilities. Research emphasizes technical challenges of such tools but also mentions severe usability shortcomings. These shortcomings hinder the adoption of static analysis tools, and user dissatisfaction may even lead to tool abandonment. To comprehensively assess the state of the art, we present the first systematic usability evaluation of a wide range of static analysis tools. We derived a set of 36 relevant criteria from the literature and used them to evaluate a total of 46 static analysis tools complying with our inclusion and exclusion criteria - a representative set of mainly non-proprietary tools. The evaluation against the usability criteria in a multiple-raters approach shows that two thirds of the considered tools off er poor warning messages, while about three-quarters provide hardly any fix support. Furthermore, the integration of user knowledge is strongly neglected, which could be used for instance, to improve handling of false positives. Finally, issues regarding workflow integration and specialized user interfaces are revealed. These findings should prove useful in guiding and focusing further research and development in user experience for static code analyses. AU - Nachtigall, Marcus AU - Schlichtig, Michael AU - Bodden, Eric ID - 52662 KW - Automated static analysis KW - Software usability SN - 978-3-88579-726-5 T2 - Software Engineering 2023 TI - Evaluation of Usability Criteria Addressed by Static Analysis Tools on a Large Scale ER - TY - CHAP AB - Application Programming Interfaces (APIs) are the primary mechanism developers use to obtain access to third-party algorithms and services. Unfortunately, APIs can be misused, which can have catastrophic consequences, especially if the APIs provide security-critical functionalities like cryptography. Understanding what API misuses are, and how they are caused, is important to prevent them, eg, with API misuse detectors. However, definitions for API misuses and related terms in literature vary. This paper presents a systematic literature review to clarify these terms and introduces FUM, a novel Framework for API Usage constraint and Misuse classification. The literature review revealed that API misuses are violations of API usage constraints. To address this, we provide unified definitions and use them to derive FUM. To assess the extent to which FUM aids in determining and guiding the improvement of an API misuses detector’s capabilities, we performed a case study on the state-of the-art misuse detection tool CogniCrypt. The study showed that FUM can be used to properly assess CogniCrypt’s capabilities, identify weaknesses and assist in deriving mitigations and improvements. AU - Schlichtig, Michael AU - Sassalla, Steffen AU - Narasimhan, Krishna AU - Bodden, Eric ID - 52660 KW - API misuses API usage constraints KW - classification framework KW - API misuse detection KW - static analysis SN - 978-3-88579-726-5 T2 - Software Engineering 2023 TI - Introducing FUM: A Framework for API Usage Constraint and Misuse Classification ER - TY - JOUR AU - Celledoni, Elena AU - Glöckner, Helge AU - Riseth, Jørgen AU - Schmeding, Alexander ID - 34803 JF - BIT Numerical Mathematics TI - Deep neural networks on diffeomorphism groups for optimal shape reparametrization VL - 63 ER - TY - JOUR AU - Glöckner, Helge AU - Hilgert, Joachim ID - 34793 JF - Journal of Differential Equations KW - 22E65 KW - 28B05 KW - 34A12 KW - 34H05 KW - 46E30 KW - 46E40 SN - 0022-0396 TI - Aspects of control theory on infinite-dimensional Lie groups and G-manifolds VL - 343 ER - TY - JOUR AB - Let $E$ be a finite-dimensional real vector space and $M\subseteq E$ be a convex polytope with non-empty interior. We turn the group of all $C^\infty$-diffeomorphisms of $M$ into a regular Lie group. AU - Glöckner, Helge ID - 34805 IS - 1 JF - Journal of Convex Analysis TI - Diffeomorphism groups of convex polytopes VL - 30 ER - TY - JOUR AU - Glöckner, Helge AU - Tárrega, Luis ID - 34801 IS - 1 JF - Journal of Lie Theory TI - Mapping groups associated with real-valued function spaces and direct limits of Sobolev-Lie groups VL - 33 ER - TY - JOUR AB - System-level interconnects provide the backbone for increasingly complex systems on a chip. Their vulnerability to electromigration and crosstalk can lead to serious reliability and safety issues during the system lifetime. This article presents an approach for periodic in-system testing which maintains a reliability profile to detect potential problems before they actually cause a failure. Relying on a common infrastructure for EM-aware system workload management and test, it minimizes the stress induced by the test itself and contributes to the self-healing of system-induced electromigration degradations. AU - Sadeghi-Kohan, Somayeh AU - Hellebrand, Sybille AU - Wunderlich, Hans-Joachim ID - 46264 JF - IEEE Design &Test KW - Electrical and Electronic Engineering KW - Hardware and Architecture KW - Software SN - 2168-2356 TI - Workload-Aware Periodic Interconnect BIST ER - TY - CONF AU - Jafarzadeh, Hanieh AU - Klemme, Florian AU - Reimer, Jan Dennis AU - Najafi Haghi, Zahra Paria AU - Amrouch, Hussam AU - Hellebrand, Sybille AU - Wunderlich, Hans-Joachim ID - 45830 T2 - IEEE International Test Conference (ITC'23), Anaheim, USA, October 2023 TI - Robust Pattern Generation for Small Delay Faults under Process Variations ER - TY - BOOK ED - Gräßler, Iris ED - Maier, Günter W. ED - Steffen, Eckhard ED - Roesmann, Daniel ID - 45191 SN - 9783031261039 TI - The Digital Twin of Humans ER -