@book{47547, editor = {{Kalenborn, Axel and Fazal-Baqaie, Masud and Linssen, Oliver and Volland, Alexander and Yigitbas, Enes and Engstler, Martin and Bertram, Martin}}, publisher = {{Gesellschaft für Informatik e.V}}, title = {{{Projektmanagement Und Vorgehensmodelle 2023 - Nachhaltige IT-Projekte}}}, volume = {{Vol. P340}}, year = {{2023}}, } @article{47800, abstract = {{The introduction of Systems Engineering is an approach for dealing with the increasing complexity of products and their associated product development. Several introduction strategies are available in the literature; nevertheless, the introduction of Systems Engineering into practice still poses a great challenge to companies. Many companies have already gained experience in the introduction of Systems Engineering. Therefore, as part of the SE4OWL research project, the need to conduct a study including expert interviews and to collect the experiences of experts was identified. A total of 78 hypotheses were identified from 13 expert interviews concerning the lessons learned. Using exclusion criteria, 52 hypotheses were validated in a subsequent quantitative survey with 112 participants. Of these 52 hypotheses, 40 could be confirmed based on the survey results. Only four hypotheses were rejected, and eight could neither be confirmed nor rejected. Through this research, guidance is provided to companies to leverage best practices for the introduction of their own Systems Engineering and to avoid the poor practices of other companies.}}, author = {{Wilke, Daria and Grothe, Robin and Bretz, Lukas and Anacker, Harald and Dumitrescu, Roman}}, issn = {{2079-8954}}, journal = {{Systems}}, keywords = {{Information Systems and Management, Computer Networks and Communications, Modeling and Simulation, Control and Systems Engineering, Software}}, number = {{3}}, publisher = {{MDPI AG}}, title = {{{Lessons Learned from the Introduction of Systems Engineering}}}, doi = {{10.3390/systems11030119}}, volume = {{11}}, year = {{2023}}, } @article{47798, abstract = {{Abstract In diesem Beitrag wird die soziotechnische Gestaltung einer Intelligenten Personaleinsatzplanung beim Unternehmen Miele & Cie. KG im Rahmen des Leuchtturmprojekts „InTime“ im Kompetenzzentrum Arbeitswelt.Plus beschrieben. Hierzu werden die Durchführung und Auswertung einer Interviewreihe sowie das daraus erarbeitete Soll-Konzept vorgestellt.}}, author = {{Gabriel, Stefan and Bentler, Dominik and Bansmann, Michael and Andrew Latos, Benedikt and Kühn, Arno and Dumitrescu, Roman}}, issn = {{2511-0896}}, journal = {{Zeitschrift für wirtschaftlichen Fabrikbetrieb}}, keywords = {{Management Science and Operations Research, Strategy and Management, General Engineering}}, number = {{1-2}}, pages = {{64--68}}, publisher = {{Walter de Gruyter GmbH}}, title = {{{Soziotechnische Gestaltung einer intelligenten Personaleinsatzplanung}}}, doi = {{10.1515/zwf-2023-1009}}, volume = {{118}}, year = {{2023}}, } @inbook{47797, author = {{Menzefricke, Jörn Steffen and Gabriel, Stefan and Gundlach, Thomas and Hobscheidt, Daniela and Kürpick, Christian and Schnasse, Felix and Scholtysik, Michel and Seif, Heiko and Koldewey, Christian and Dumitrescu, Roman}}, booktitle = {{Schwerpunkt Business Model Innovation}}, isbn = {{9783658366339}}, issn = {{2569-2348}}, publisher = {{Springer Fachmedien Wiesbaden}}, title = {{{Soziotechnisches Risikomanagement als Erfolgsfaktor für die Digitale Transformation}}}, doi = {{10.1007/978-3-658-36634-6_14}}, year = {{2023}}, } @inproceedings{47799, author = {{Bentler, Dominik and Gabriel, Stefan and Latos, Benedikt A. and Dietrich, Oliver and Dumitrescu, Roman and Maier, Günter W.}}, booktitle = {{GfA-Frühjahrskongress 2023}}, publisher = {{GfA, Sankt Augustin}}, title = {{{Partizipatives Gestaltungsvorgehen bei der Einführung künstlicher Intelligenz in produzierenden Unternehmen}}}, year = {{2023}}, } @article{47827, author = {{Weller, Julian and Roesmann, Daniel and Eggert, Sönke and von Enzberg, Sebastian and Gräßler, Iris and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, keywords = {{General Medicine}}, pages = {{514--520}}, publisher = {{Elsevier BV}}, title = {{{Identification and prediction of standard times in machining for precision steel tubes through the usage of data analytics}}}, doi = {{10.1016/j.procir.2023.01.011}}, volume = {{119}}, year = {{2023}}, } @article{47822, author = {{Machon, Fabian and Gabriel, Stefan and Latos, Benedikt and Holtkötter, Christoph and Lütkehoff, Ben and Asmar, Laban and Kühn, Arno and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, keywords = {{General Medicine}}, pages = {{1017--1022}}, publisher = {{Elsevier BV}}, title = {{{Design of individual simulation games in manufacturing companies for game-based learning}}}, doi = {{10.1016/j.procir.2023.03.145}}, volume = {{119}}, year = {{2023}}, } @article{47824, author = {{Brock, Jonathan and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, keywords = {{General Medicine}}, pages = {{602--607}}, publisher = {{Elsevier BV}}, title = {{{Process Mining Data Canvas: A method to identify data and process knowledge for data collection and preparation in process mining projects}}}, doi = {{10.1016/j.procir.2023.03.114}}, volume = {{119}}, year = {{2023}}, } @article{47826, author = {{Wilke, Daria and Schierbaum, Anja and Anacker, Harald and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, keywords = {{General Medicine}}, pages = {{788--793}}, publisher = {{Elsevier BV}}, title = {{{Targeted-oriented selection of engineering methods}}}, doi = {{10.1016/j.procir.2023.02.166}}, volume = {{119}}, year = {{2023}}, } @phdthesis{47833, author = {{König, Jürgen}}, title = {{{On the Membership and Correctness Problem for State Serializability and Value Opacity}}}, year = {{2023}}, } @phdthesis{47837, author = {{Hansmeier, Tim}}, title = {{{XCS for Self-awareness in Autonomous Computing Systems}}}, year = {{2023}}, } @inproceedings{32407, abstract = {{Estimating the ground state energy of a local Hamiltonian is a central problem in quantum chemistry. In order to further investigate its complexity and the potential of quantum algorithms for quantum chemistry, Gharibian and Le Gall (STOC 2022) recently introduced the guided local Hamiltonian problem (GLH), which is a variant of the local Hamiltonian problem where an approximation of a ground state is given as an additional input. Gharibian and Le Gall showed quantum advantage (more precisely, BQP-completeness) for GLH with $6$-local Hamiltonians when the guiding vector has overlap (inverse-polynomially) close to 1/2 with a ground state. In this paper, we optimally improve both the locality and the overlap parameters: we show that this quantum advantage (BQP-completeness) persists even with 2-local Hamiltonians, and even when the guiding vector has overlap (inverse-polynomially) close to 1 with a ground state. Moreover, we show that the quantum advantage also holds for 2-local physically motivated Hamiltonians on a 2D square lattice. This makes a further step towards establishing practical quantum advantage in quantum chemistry.}}, author = {{Gharibian, Sevag and Hayakawa, Ryu and Gall, François Le and Morimae, Tomoyuki}}, booktitle = {{Proceedings of the 50th EATCS International Colloquium on Automata, Languages and Programming (ICALP)}}, number = {{32}}, pages = {{1--19}}, title = {{{Improved Hardness Results for the Guided Local Hamiltonian Problem}}}, doi = {{10.4230/LIPIcs.ICALP.2023.32}}, volume = {{261}}, year = {{2023}}, } @article{48051, author = {{Humpert, Lynn and Wäschle, Moritz and Horstmeyer, Sarah and Anacker, Harald and Dumitrescu, Roman and Albers, Albert}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, keywords = {{General Medicine}}, pages = {{693--698}}, publisher = {{Elsevier BV}}, title = {{{Stakeholder-oriented Elaboration of Artificial Intelligence use cases using the example of Special-Purpose engineering}}}, doi = {{10.1016/j.procir.2023.02.160}}, volume = {{119}}, year = {{2023}}, } @inproceedings{47522, abstract = {{Artificial benchmark functions are commonly used in optimization research because of their ability to rapidly evaluate potential solutions, making them a preferred substitute for real-world problems. However, these benchmark functions have faced criticism for their limited resemblance to real-world problems. In response, recent research has focused on automatically generating new benchmark functions for areas where established test suites are inadequate. These approaches have limitations, such as the difficulty of generating new benchmark functions that exhibit exploratory landscape analysis (ELA) features beyond those of existing benchmarks.The objective of this work is to develop a method for generating benchmark functions for single-objective continuous optimization with user-specified structural properties. Specifically, we aim to demonstrate a proof of concept for a method that uses an ELA feature vector to specify these properties in advance. To achieve this, we begin by generating a random sample of decision space variables and objective values. We then adjust the objective values using CMA-ES until the corresponding features of our new problem match the predefined ELA features within a specified threshold. By iteratively transforming the landscape in this way, we ensure that the resulting function exhibits the desired properties. To create the final function, we use the resulting point cloud as training data for a simple neural network that produces a function exhibiting the target ELA features. We demonstrate the effectiveness of this approach by replicating the existing functions of the well-known BBOB suite and creating new functions with ELA feature values that are not present in BBOB.}}, author = {{Prager, Raphael Patrick and Dietrich, Konstantin and Schneider, Lennart and Schäpermeier, Lennart and Bischl, Bernd and Kerschke, Pascal and Trautmann, Heike and Mersmann, Olaf}}, booktitle = {{Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{9798400702020}}, keywords = {{Benchmarking, Instance Generator, Black-Box Continuous Optimization, Exploratory Landscape Analysis, Neural Networks}}, pages = {{129–139}}, publisher = {{Association for Computing Machinery}}, title = {{{Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features}}}, doi = {{10.1145/3594805.3607136}}, year = {{2023}}, } @inproceedings{46297, abstract = {{Exploratory landscape analysis (ELA) in single-objective black-box optimization relies on a comprehensive and large set of numerical features characterizing problem instances. Those foster problem understanding and serve as basis for constructing automated algorithm selection models choosing the best suited algorithm for a problem at hand based on the aforementioned features computed prior to optimization. This work specifically points to the sensitivity of a substantial proportion of these features to absolute objective values, i.e., we observe a lack of shift and scale invariance. We show that this unfortunately induces bias within automated algorithm selection models, an overfitting to specific benchmark problem sets used for training and thereby hinders generalization capabilities to unseen problems. We tackle these issues by presenting an appropriate objective normalization to be used prior to ELA feature computation and empirically illustrate the respective effectiveness focusing on the BBOB benchmark set.}}, author = {{Prager, Raphael Patrick and Trautmann, Heike}}, booktitle = {{Applications of Evolutionary Computation}}, editor = {{Correia, João and Smith, Stephen and Qaddoura, Raneem}}, isbn = {{978-3-031-30229-9}}, pages = {{411–425}}, publisher = {{Springer Nature Switzerland}}, title = {{{Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features}}}, year = {{2023}}, } @inproceedings{46298, abstract = {{The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good understanding of their Pareto sets and fronts, such as the well-known DTLZ and ZDT problems. However, they lack diversity in their landscape properties and do not provide a mechanism for creating multiple distinct problem instances. Other suites, like bi-objective BBOB, possess diverse and challenging landscape properties, but their optima are not well understood and can only be approximated empirically without any guarantees.}}, author = {{Schäpermeier, Lennart and Kerschke, Pascal and Grimme, Christian and Trautmann, Heike}}, booktitle = {{Evolutionary Multi-Criterion Optimization}}, editor = {{Emmerich, Michael and Deutz, André and Wang, Hao and Kononova, Anna V. and Naujoks, Boris and Li, Ke and Miettinen, Kaisa and Yevseyeva, Iryna}}, isbn = {{978-3-031-27250-9}}, pages = {{291–304}}, publisher = {{Springer Nature Switzerland}}, title = {{{Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets}}}, year = {{2023}}, } @article{46299, abstract = {{The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features is already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization.}}, author = {{Prager, Raphael Patrick and Trautmann, Heike}}, issn = {{1063-6560}}, journal = {{Evolutionary Computation}}, pages = {{1–25}}, title = {{{Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python}}}, doi = {{10.1162/evco_a_00341}}, year = {{2023}}, } @inproceedings{48289, author = {{Habernal, Ivan and Mireshghallah, Fatemehsadat and Thaine, Patricia and Ghanavati, Sepideh and Feyisetan, Oluwaseyi}}, booktitle = {{Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts}}, publisher = {{Association for Computational Linguistics}}, title = {{{Privacy-Preserving Natural Language Processing}}}, doi = {{10.18653/v1/2023.eacl-tutorials.6}}, year = {{2023}}, } @inproceedings{48288, author = {{Matzken, Cleo and Eger, Steffen and Habernal, Ivan}}, booktitle = {{Findings of the Association for Computational Linguistics: ACL 2023}}, publisher = {{Association for Computational Linguistics}}, title = {{{Trade-Offs Between Fairness and Privacy in Language Modeling}}}, doi = {{10.18653/v1/2023.findings-acl.434}}, year = {{2023}}, } @inproceedings{48291, author = {{Mouhammad, Nina and Daxenberger, Johannes and Schiller, Benjamin and Habernal, Ivan}}, booktitle = {{Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)}}, publisher = {{Association for Computational Linguistics}}, title = {{{Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting}}}, doi = {{10.18653/v1/2023.law-1.8}}, year = {{2023}}, }