@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for Predictive Maintenance: One Tool to RUL Them All}}}, doi = {{10.1007/978-3-030-66770-2_8}}, year = {{2020}}, } @unpublished{17605, abstract = {{Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent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}}, } @unpublished{17825, abstract = {{Software verification has recently made enormous progress due to the development of novel verification methods and the speed-up of supporting technologies like SMT solving. To keep software verification tools up to date with these advances, tool developers keep on integrating newly designed methods into their tools, almost exclusively by re-implementing the method within their own framework. While this allows for a conceptual re-use of methods, it requires novel implementations for every new technique. In this paper, we employ cooperative verification in order to avoid reimplementation and enable usage of novel tools as black-box components in verification. Specifically, cooperation is employed for the core ingredient of software verification which is invariant generation. Finding an adequate loop invariant is key to the success of a verification run. Our framework named CoVerCIG allows a master verification tool to delegate the task of invariant generation to one or several specialized helper invariant generators. Their results are then utilized within the verification run of the master verifier, allowing in particular for crosschecking the validity of the invariant. We experimentally evaluate our framework on an instance with two masters and three different invariant generators using a number of benchmarks from SV-COMP 2020. The experiments show that the use of CoVerCIG can increase the number of correctly verified tasks without increasing the used resources}}, author = {{Haltermann, Jan Frederik and Wehrheim, Heike}}, booktitle = {{arXiv:2008.04551}}, title = {{{Cooperative Verification via Collective Invariant Generation}}}, year = {{2020}}, } @proceedings{17836, editor = {{Werneck Richa, Andrea and Scheideler, Christian}}, isbn = {{978-3-030-54920-6}}, publisher = {{Springer}}, title = {{{Structural Information and Communication Complexity - 27th International Colloquium, SIROCCO 2020, Paderborn, Germany, June 29 - July 1, 2020, Proceedings}}}, doi = {{10.1007/978-3-030-54921-3}}, volume = {{12156}}, year = {{2020}}, } @proceedings{17839, editor = {{Scheideler, Christian and Spear, Michael}}, isbn = {{978-1-4503-6935-0}}, publisher = {{ACM}}, title = {{{SPAA '20: 32nd ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, USA, July 15-17, 2020}}}, doi = {{10.1145/3350755}}, year = {{2020}}, } @inbook{18789, author = {{Nickchen, Tobias and Engels, Gregor and Lohn, Johannes}}, booktitle = {{Industrializing Additive Manufacturing}}, isbn = {{9783030543334}}, title = {{{Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain}}}, doi = {{10.1007/978-3-030-54334-1_4}}, year = {{2020}}, } @inproceedings{18876, author = {{Reinhold, Jannik and Frank, Maximilian and Koldewey, Christian and Dumitrescu, Roman and Buss, Eugen}}, booktitle = {{Proceedings of the ISPIM Connects Bangkok – Partnering for an Innovative Community}}, publisher = {{LUT Scientific and Expertise Publications}}, title = {{{In-depth Analysis of the Effects of Smart Services on Value Creation}}}, year = {{2020}}, } @inproceedings{20305, author = {{Menzefricke, Jörn Steffen and Frank, Maximilian and Drewel, Marvin and Dumitrescu, Roman}}, booktitle = {{Procedia CIRP}}, editor = {{Mpofu, Khumbulani and Butala, Peter}}, pages = {{690--695}}, title = {{{Value-centered design of a digital service robotics platform}}}, volume = {{91}}, 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{20307, author = {{Schnasse, Felix and Menzefricke, Jörn Steffen and Gabriel, Stefan and Hobscheidt, Daniela and Parlings, Matthias and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Proceedings of the Hamburg International Conference of Logistics (HICL)}}, title = {{{Identification of socio-technical changes caused by Industry 4.0}}}, volume = {{29}}, year = {{2020}}, } @inproceedings{20335, author = {{Winkelnkemper, Felix and Schulte, Carsten and Eilerts, Katja and Bechinie, Dominik and Huhmann, Tobias}}, booktitle = {{EdMedia+ Innovate Learning}}, pages = {{522--529}}, title = {{{The Interdisciplinary Development of an Educational Game for Primary School Children--Lessons Learned}}}, year = {{2020}}, } @inproceedings{20355, author = {{Koldewey, Christian and Gausemeier, Jürgen and Chohan, Nadia and Frank, Maximilian and Reinhold, Jannik and Dumitrescu, Roman}}, booktitle = {{Proceedings of the IEEE International Conference on Technology Management, Operations and Decisions ”Disruptive Technologies and Social Impacts”}}, location = {{Marrakesch, Marocco}}, title = {{{Aligning Strategy and Structure for Smart Service Businesses in Manufacturing}}}, year = {{2020}}, } @article{20363, abstract = {{Die Verfügbarkeit von Daten aus dem Betrieb hat durch die Einführung von vernetzten intelligenten Fertigungssystemen (cyber-physische Systeme) stark zugenommen. Hieraus eröffnen sich Erfolg versprechende Möglichkeiten für Smart Services und damit verbundene neue Geschäftsfelder. Voraussetzung für den Eintritt in ein neues Geschäft mit Smart Services ist eine fundierte Geschäftsstrategie. Wir zeigen die zentralen Gestaltungsfelder von Smart Service-Strategien auf und erläutern, wie sechs von uns ermittelte Normstrategien bei der Formulierung einer attraktiven Strategie helfen können.}}, author = {{Koldewey, Christian and Frank, Maximilian and Gausemeier, Jürgen and Bäsecke, Alexander and Reinhold, Jannik and Dumitrescu, Roman}}, journal = {{ZWF Zeitschrift für wirtschaftliche Fabrikplanung}}, keywords = {{Smart Service, Digitalisierung, Industrie 4.0}}, number = {{7-8}}, pages = {{524--528}}, publisher = {{Hanser}}, title = {{{Systematische Entwicklung von Normstrategien für Smart Services}}}, doi = {{10.3139/104.112297}}, volume = {{115}}, year = {{2020}}, } @inbook{20365, abstract = {{Today’s manufacturing industry is confronted with fundamental changes in value creation. The tension between the two megatrends of digitization and servitization leads to new hybrid market offerings, so-called smart services. Corresponding business models and value networks fundamentally differ from traditional ones. Developing smart services requires an advanced management of business models and new competences in young disciplines, while their provision requires new internal and external organizational structures or processes. To strengthen their competitive position, manufacturing companies need to extend their business model portfolios and adapt their value networks. However, the highly complex transformation of value creation especially challenges small and medium-sized companies due to limited competences and resources. They must consider opening their boundaries and collaborating with partners. In this chapter, we introduce a basic framework for designing smart services and present a methodology for the planning of a smart service business integrating external partners. The methodology is structured into five phases: Examination of smart service portfolio, analysis of business models and environment, business model portfolio planning, competence identification and analysis, and value creation planning. The methodology is explained by an example on cable marker printers.}}, author = {{Koldewey, Christian and Reinhold, Jannik and Dumitrescu, Roman}}, booktitle = {{Managing Digital Open Innovation}}, editor = {{Barlatier, Pierre-Jean and Mention, Anne-Laure}}, isbn = {{978-981-121-923-8}}, keywords = {{Smart Service, Digitalisierung, Industrie 4.0}}, pages = {{255--298}}, publisher = {{World Scientific Publishing Company}}, title = {{{Planning a Smart Service Business Integrating External Partners}}}, doi = {{10.1142/9789811219238_0010}}, volume = {{Volume 5}}, year = {{2020}}, } @article{20447, author = {{Massmann, Melina and Meyer, Maurice and Frank, Maximilian and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, journal = {{Procedia CIRP}}, number = {{93}}, pages = {{234--239}}, title = {{{Method for data inventory and classification}}}, year = {{2020}}, } @article{20448, author = {{Meyer, Maurice and Frank, Maximilian and Massmann, Melina and Wendt, Niklas and Dumitrescu, Roman}}, journal = {{Procedia CIRP}}, number = {{93}}, pages = {{965--970}}, title = {{{Data-Driven Product Generation and Retrofit Planning}}}, year = {{2020}}, } @article{20449, author = {{Uhlmann, Eckart and Dumitrescu, Roman and Polte, Julian and Meyer, Maurice and Simsek, Deniz}}, journal = {{ wt Werkstattstechnik online }}, number = {{07-08}}, pages = {{532--535}}, title = {{{Datengetriebene Steigerung der Verfügbarkeit}}}, volume = {{110}}, year = {{2020}}, } @article{20450, author = {{Massmann, Melina and Meyer, Maurice and Frank, Maximilian and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, journal = {{Procedia Manufacturing}}, title = {{{Framework for Data Analytics in Data-Driven Product Planning}}}, year = {{2020}}, }