@phdthesis{28369, author = {{Lukei, Meinolf}}, isbn = {{978-3-947647-14-9}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Systematik zur integrativen Entwicklung von mechatronischen Produkten und deren Prüfmittel}}}, volume = {{395}}, year = {{2020}}, } @inproceedings{17407, author = {{Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Discovery Science}}, title = {{{Extreme Algorithm Selection with Dyadic Feature Representation}}}, year = {{2020}}, } @inproceedings{17408, author = {{Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{KI 2020: Advances in Artificial Intelligence}}, title = {{{Hybrid Ranking and Regression for Algorithm Selection}}}, year = {{2020}}, } @inproceedings{17424, author = {{Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the ECMLPKDD 2020}}, title = {{{AutoML for Predictive Maintenance: One Tool to RUL Them All}}}, doi = {{10.1007/978-3-030-66770-2_8}}, year = {{2020}}, } @unpublished{17605, abstract = {{Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. While the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography. These irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small. In our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.}}, author = {{Heid, Stefan Helmut and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Journal of Data Mining and Digital Humanities}}, publisher = {{episciences}}, title = {{{Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction}}}, year = {{2020}}, } @inproceedings{20306, author = {{Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Workshop MetaLearn 2020 @ NeurIPS 2020}}, location = {{Online}}, title = {{{Towards Meta-Algorithm Selection}}}, year = {{2020}}, } @inproceedings{18276, abstract = {{Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Due to possibly extremely long runtimes of candidate algorithms, training data for algorithm selection models is usually generated under time constraints in the sense that not all algorithms are run to completion on all instances. Thus, training data usually comprises censored information, as the true runtime of algorithms timed out remains unknown. However, many standard AS approaches are not able to handle such information in a proper way. On the other side, survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime, as we demonstrate in this work. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of a framework of this kind, we advocate a risk-averse approach to algorithm selection, in which the avoidance of a timeout is given high priority. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.}}, author = {{Tornede, Alexander and Wever, Marcel Dominik and Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{ACML 2020}}, location = {{Bangkok, Thailand}}, title = {{{Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis}}}, year = {{2020}}, } @inproceedings{15629, abstract = {{In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.}}, author = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}}, location = {{Konstanz, Germany}}, publisher = {{Springer}}, title = {{{LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification}}}, year = {{2020}}, } @article{15025, abstract = {{In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.}}, author = {{Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}}, journal = {{Evolutionary Computation}}, number = {{2}}, pages = {{165–193}}, publisher = {{MIT Press Journals}}, title = {{{Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets}}}, doi = {{10.1162/evco_a_00266}}, volume = {{28}}, year = {{2020}}, } @phdthesis{28368, author = {{Lochbichler, Matthias}}, isbn = {{978-3-947647-13-2}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Systematische Wahl einer Modellierungstiefe im Entwurfsprozess mechatronischer Systeme}}}, volume = {{394}}, year = {{2020}}, } @phdthesis{28363, author = {{Mittag, Tobias}}, isbn = {{978-3-947647-08-8}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Systematik zur Gestaltung der Wertschöpfung für digitalisierte hybride Marktleistungen}}}, volume = {{389}}, year = {{2019}}, } @misc{28364, author = {{Gausemeier, Jürgen}}, isbn = {{978-3-947647-09-5}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Vorausschau und Technologieplanung. 15. Symposium für Vorausschau und Technologieplanung, Heinz Nixdorf Institut, 21. und 22. November 2019}}}, volume = {{390}}, year = {{2019}}, } @phdthesis{28365, author = {{Schierbaum, Anja Maria}}, isbn = {{978-3-947647-10-1}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Systematik zur Ableitung bedarfsgerechter Systems Engineering Leitfäden im Maschinenbau}}}, volume = {{391}}, year = {{2019}}, } @article{17565, author = {{Merten, Marie-Luis and Seemann, Nina and Wever, Marcel Dominik}}, journal = {{Niederdeutsches Jahrbuch}}, number = {{142}}, pages = {{124--146}}, title = {{{Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff}}}, year = {{2019}}, } @inproceedings{22000, abstract = {{Requirement changes are a major cause for project failure. A systematic approach to manage those changes from the very beginning should be an in-tegral part of each development project. Although this is accepted in both sci-ence and industry, there is no adequate approach to tackle the issue, especially in the context of interdisciplinary systems. In this paper, a secondary analysis is done to identify all information that is necessary to manage those changes efficiently. The demanded information is pictured in a reference model and then mapped with the capabilities of existing approaches. Based on this, research gaps are identified and used to guide future research efforts. }}, author = {{Gräßler, I. and Oleff, C.}}, booktitle = {{Design for X - Beiträge zum 30. DfX-Symposium }}, pages = {{49--60}}, title = {{{Risikoorientierte Analyse und Handhabung von Anforderungsänderungen}}}, doi = {{ 10.35199/dfx2019.5}}, volume = {{30}}, year = {{2019}}, } @inproceedings{22001, abstract = {{In diesem Beitrag wird ein Ansatz vorgestellt, welcher die Bewertung des Risikos von Anforderungsänderungen in der Entwicklung mechatronischer Systeme ermöglicht. Ausgehend von einer Anforderungsliste werden die Wechselwirkungen in einer Requirements Structure Matrix (RSM) teilautomatisch erfasst. Parallel werden Anforderungen in Bezug auf ihren Ursprung („Einflussbereich“) kategorisiert und darauf aufbauend priorisiert. Diese Priorisierung basiert auf dem Veränderungsrisiko und wird durch die drei Kriterien „Dynamik“, „Unsicherheit der Wissensbasis“ und „Relevanz für den Entwicklungsprozess“ charakterisiert. Das Vorgehen wird anhand strukturierter Interviews mit Projektleitern und Entwicklern und der Fallstudie eines Pedelecs als mechatronischem System validiert. Durch die Anwendung der Methode können disziplinübergreifende Abhängigkeiten von Anforderungen zur Reduktion von Iterationen in der Entwicklung mechatronischer Systeme – wie dem Pedelec – berücksichtigt werden.}}, author = {{Gräßler, I. and Oleff, C. and Scholle, P.}}, booktitle = {{Fachtagung Mechatronik 2019 Paderborn}}, pages = {{S. 1--6}}, title = {{{Priorisierung von Anforderungen für die Entwicklung mechatronischer Systeme}}}, doi = {{ 10.17619/UNIPB/1-791}}, year = {{2019}}, } @inproceedings{22002, abstract = {{In diesem Beitrag wird ein Ansatz vorgestellt, welcher die Bewertung des Risikos von Anforderungsänderungen in der Entwicklung mechatronischer Systeme ermöglicht. Ausgehend von einer Anforderungsliste werden die Wechselwirkungen in einer Requirements Structure Matrix (RSM) teilautomatisch erfasst. Parallel werden Anforderungen in Bezug auf ihren Ursprung („Einflussbereich“) kategorisiert und darauf aufbauend priorisiert. Diese Priorisierung basiert auf dem Veränderungsrisiko und wird durch die drei Kriterien „Dynamik“, „Unsicherheit der Wissensbasis“ und „Relevanz für den Entwicklungsprozess“ charakterisiert. Das Vorgehen wird anhand strukturierter Interviews mit Projektleitern und Entwicklern und der Fallstudie eines Pedelecs als mechatronischem System validiert. Durch die Anwendung der Methode können disziplinübergreifende Abhängigkeiten von Anforderungen zur Reduktion von Iterationen in der Entwicklung mechatronischer Systeme – wie dem Pedelec – berücksichtigt werden.}}, author = {{Gräßler, I. and Thiele, H. and Oleff, C. and Scholle, P. and Schulze, V.}}, booktitle = {{International Conference on Engineering Design (ICED19)}}, pages = {{1265--1274}}, title = {{{Priorisierung von Anforderungen für die Entwicklung mechatronischer Systeme}}}, doi = {{10.17619/UNIPB/1-791}}, year = {{2019}}, } @article{14896, author = {{Dann, Andreas and Hermann, Ben and Bodden, Eric}}, issn = {{0098-5589}}, journal = {{IEEE Transactions on Software Engineering}}, pages = {{1--1}}, title = {{{ModGuard: Identifying Integrity &Confidentiality Violations in Java Modules}}}, doi = {{10.1109/tse.2019.2931331}}, year = {{2019}}, } @inproceedings{14897, author = {{Dann, Andreas and Hermann, Ben and Bodden, Eric}}, booktitle = {{Proceedings of the 8th ACM SIGPLAN International Workshop on State Of the Art in Program Analysis - SOAP 2019}}, isbn = {{9781450367202}}, title = {{{SootDiff: bytecode comparison across different Java compilers}}}, doi = {{10.1145/3315568.3329966}}, year = {{2019}}, } @inproceedings{14899, author = {{Kruger, Stefan and Hermann, Ben}}, booktitle = {{2019 IEEE/ACM 2nd International Workshop on Gender Equality in Software Engineering (GE)}}, isbn = {{9781728122458}}, title = {{{Can an Online Service Predict Gender? On the State-of-the-Art in Gender Identification from Texts}}}, doi = {{10.1109/ge.2019.00012}}, year = {{2019}}, }