@article{21337,
  abstract     = {{We present a flexible trust region descend algorithm for unconstrained and
convexly constrained multiobjective optimization problems. It is targeted at
heterogeneous and expensive problems, i.e., problems that have at least one
objective function that is computationally expensive. The method is
derivative-free in the sense that neither need derivative information be
available for the expensive objectives nor are gradients approximated using
repeated function evaluations as is the case in finite-difference methods.
Instead, a multiobjective trust region approach is used that works similarly to
its well-known scalar pendants. Local surrogate models constructed from
evaluation data of the true objective functions are employed to compute
possible descent directions. In contrast to existing multiobjective trust
region algorithms, these surrogates are not polynomial but carefully
constructed radial basis function networks. This has the important advantage
that the number of data points scales linearly with the parameter space
dimension. The local models qualify as fully linear and the corresponding
general scalar framework is adapted for problems with multiple objectives.
Convergence to Pareto critical points is proven and numerical examples
illustrate our findings.}},
  author       = {{Berkemeier, Manuel Bastian and Peitz, Sebastian}},
  issn         = {{2297-8747}},
  journal      = {{Mathematical and Computational Applications}},
  number       = {{2}},
  title        = {{{Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models}}},
  doi          = {{10.3390/mca26020031}},
  volume       = {{26}},
  year         = {{2021}},
}

@inproceedings{21543,
  abstract     = {{Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks.

To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).}},
  author       = {{Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}},
  booktitle    = {{IEEE International Conference on Distributed Computing Systems (ICDCS)}},
  keywords     = {{network management, service management, coordination, reinforcement learning, distributed}},
  location     = {{Washington, DC, USA}},
  publisher    = {{IEEE}},
  title        = {{{Distributed Online Service Coordination Using Deep Reinforcement Learning}}},
  year         = {{2021}},
}

@techreport{21569,
  abstract     = {{Die kontinuierliche Weiterentwicklung des eigenen Geschäftsmodells ist für eine Organisation von entscheidender Bedeutung, um wettbewerbsfähig und somit nachhaltig erfolgreich zu bleiben. Während für die Entwicklung neuer Geschäftsmodelle häufig Workshops und einfache Software-Tools zur Visualisierung genutzt werden, wurden in der Forschung bereits erste Ansätze von datengetriebener Geschäftsmodellentwicklung (GME) vorgestellt. Diese Ansätze nutzen dabei Daten, Informationen oder auch Wissen aus internen und externen Unternehmensquellen, um den GME-Prozess zu unterstützen. Innerhalb dieses Beitrags zeigen wir einige Ansätze aus der aktuellen Literatur und analysieren wie ihre Datennutzung den GME-Prozess unterstützt. Weiterhin stellen wir mit dem BMDL Feature Modeler ein Tool vor, welches den GME-Prozess mit Expertenwissen unterstützt.}},
  author       = {{Gottschalk, Sebastian and Yigitbas, Enes}},
  publisher    = {{Gesellschaft für Informatik}},
  title        = {{{Von datenbasierter zu datengetriebener Geschäftsmodellentwicklung: Ein Überblick über Software-Tools  und deren Datennutzung}}},
  volume       = {{1}},
  year         = {{2021}},
}

@misc{21601,
  abstract     = {{The invention describes a distributed merchandise management system, in which the client, retailer and the manufacturer are linked by a network. This is implemented by a cloud storage (105), the cloud storage (105) comprising a means (105 a) for storing data, a means for receiving first data from a first network node (110), the first data being associated with a physical object, a means for receiving request data from a second network node (120), a means for receiving second data from a third network node (130), the second data being associated with the first data and comprising at least one data piece adapted to change the first data depending on the received request data, a means for changing the first data based at least in part on the second data and the request data, and a means for sending a changed portion of the first data from the cloud storage (105) to the first network node (110).}},
  author       = {{Göllner, Thomas and Schwarz, Jan-Hendrik and Gottschalk, Sebastian and Sauer, Stefan}},
  title        = {{{Distributed merchandise management system}}},
  year         = {{2021}},
}

@article{21631,
  abstract     = {{<jats:p>Secret sharing is a well-established cryptographic primitive for storing highly sensitive information like encryption keys for encoded data. It describes the problem of splitting a secret into different shares, without revealing any information to its shareholders. Here, we demonstrate an all-optical solution for secret sharing based on metasurface holography. In our concept, metasurface holograms are used as spatially separable shares that carry encrypted messages in the form of holographic images. Two of these shares can be recombined by bringing them close together. Light passing through this stack of metasurfaces accumulates the phase shift of both holograms and optically reconstructs the secret with high fidelity. In addition, the hologram generated by each single metasurface can uniquely identify its shareholder. Furthermore, we demonstrate that the inherent translational alignment sensitivity between two stacked metasurface holograms can be used for spatial multiplexing, which can be further extended to realize optical rulers.</jats:p>}},
  author       = {{Georgi, Philip and Wei, Qunshuo and Sain, Basudeb and Schlickriede, Christian and Wang, Yongtian and Huang, Lingling and Zentgraf, Thomas}},
  issn         = {{2375-2548}},
  journal      = {{Science Advances}},
  number       = {{16}},
  title        = {{{Optical secret sharing with cascaded metasurface holography}}},
  doi          = {{10.1126/sciadv.abf9718}},
  volume       = {{7}},
  year         = {{2021}},
}

@inproceedings{21637,
  author       = {{Lienen, Julian and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI}},
  location     = {{Online}},
  number       = {{10}},
  pages        = {{8583--8591}},
  publisher    = {{AAAI Press}},
  title        = {{{From Label Smoothing to Label Relaxation}}},
  volume       = {{35}},
  year         = {{2021}},
}

@inproceedings{21639,
  abstract     = {{The development of effective business models is an essential task in highly competitive markets like mobile ecosystems. Existing development methods for these business models do not specifically focus that the development process profoundly depends on the situation (e.g., market size, regulations) of the mobile app developer. Here, a mismatch between method and situation can lead to poor resource management and longer development cycles. In software engineering, situational method engineering is used for software projects to configure a development method out of a method repository based on the project situation. Analogously, we support creating situation-specific business model development methods with a method base and new user roles. Here, the method engineer obtains the knowledge of the domain expert and stores it in the method base as elements, building blocks, and patterns. The expert knowledge is derived from a grey literature review on mobile development processes. After this, the method engineer constructs the development method based on the described situation of the business developer. We provide an open-source tool and evaluate it by constructing a local event platform's business model development method.    }},
  author       = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}},
  booktitle    = {{Enterprise, Business-Process and Information Systems Modeling}},
  keywords     = {{Business Model Development, Situational Method Engineering, Mobile App, Business Model Development Tools}},
  publisher    = {{Springer}},
  title        = {{{Situation-specific Business Model Development Methods for Mobile App Developers}}},
  doi          = {{10.1007/978-3-030-79186-5_17}},
  year         = {{2021}},
}

@article{23526,
  abstract     = {{<jats:p>Modern and flexible application-level software platforms increase the attack surface of connected vehicles and thereby require automotive engineers to adopt additional security control techniques. These techniques encompass host-based intrusion detection systems (HIDSs) that detect suspicious activities in application contexts. Such application-aware HIDSs originate in information and communications technology systems and have a great potential to deal with the flexible nature of application-level software platforms. However, the elementary characteristics of known application-aware HIDS approaches and thereby the implications for their transfer to the automotive sector are unclear. In previous work, we presented a systematic literature review (SLR) covering the state of the art of application-aware HIDS approaches. We synthesized our findings by means of a fine-grained classification for each approach specified through a feature model and corresponding variant models. These models represent the approaches’ elementary characteristics. Furthermore, we summarized key findings and inferred implications for the transfer of application-aware HIDSs to the automotive sector. In this article, we extend the previous work by several aspects. We adjust the quality evaluation process within the SLR to be able to consider high quality conference publications, which results in an extended final pool of publications. For supporting HIDS developers on the task of configuring HIDS analysis techniques based on machine learning, we report on initial results on the applicability of AutoML. Furthermore, we present lessons learned regarding the application of the feature and variant model approach for SLRs. Finally, we more thoroughly describe the SLR study design.</jats:p>}},
  author       = {{Schubert, David and Eikerling, Hendrik and Holtmann, Jörg}},
  issn         = {{2624-9898}},
  journal      = {{Frontiers in Computer Science}},
  publisher    = {{Frontiers Media}},
  title        = {{{Application-Aware Intrusion Detection: A Systematic Literature Review, Implications for Automotive Systems, and Applicability of AutoML}}},
  doi          = {{10.3389/fcomp.2021.567873}},
  volume       = {{3}},
  year         = {{2021}},
}

@inproceedings{23708,
  author       = {{Nouri, Zahra and Gadiraju, Ujwal and Engels, Gregor and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 32nd ACM Conference on Hypertext and Social Media}},
  pages        = {{165--175}},
  title        = {{{What Is Unclear? Computational Assessment of Task Clarity in Crowdsourcing}}},
  year         = {{2021}},
}

@inproceedings{23760,
  abstract     = {{The laser sintering process has been a well-established AM process for many years.
Disadvantages of LS are the low material variety and the thermal damage of the unprocessed
material. The low temperature laser sintering attacks at this point and processes powder material at
a build chamber temperature lower than the recrystallization temperature. This drastic reduction in
temperature results in significantly less thermal damage to the material. This work deals with the
low temperature laser sintering of Polyamide 12 (PA12) on a commercial, unmodified laser
sintering system to compare it to standard laser sintered PA12 and to create the basis for low
temperature laser sintering of high temperature materials on such a system. First results by
changing the exposure parameters and by fixing parts on a building platform show a processing of
PA12 on an EOS P396 at a build chamber temperature less than 100 °C instead of standard approx.
175 °C.}},
  author       = {{Menge, Dennis and Schmid, Hans-Joachim}},
  keywords     = {{Low Temp LS, Low Temperature Laser Sintering, Polyamid 12}},
  location     = {{Austin, TX}},
  title        = {{{Low Temperature Laser Sintering on a Standard System: First Attempts and Results with PA12}}},
  year         = {{2021}},
}

@article{20592,
  abstract     = {{GaAs-(111)-nanostructures exhibiting second harmonic generation are new building blocks in nonlinear optics. Such structures can be fabricated through epitaxial lift-off using selective etching of Al-containing layers and subsequent transfer to glass substrates. Herein, the selective etching of (111)B-oriented AlxGa1−xAs sacrificial layers (10–50 nm thick) with different aluminum concentrations (x = 0.5–1.0) in 10\% hydrofluoric acid is investigated and compared with standard (100)-oriented structures. The thinner the sacrificial layer and the lower the aluminum content, the lower the lateral etch rate. For both orientations, the lateral etch rates are in the same order of magnitude, but some quantitative differences exist. Furthermore, the epitaxial lift-off, the transfer, and the nanopatterning of thin (111)B-oriented GaAs membranes are demonstrated. Atomic force microscopy and high-resolution X-ray diffraction measurements reveal the high structural quality of the transferred GaAs-(111) films.}},
  author       = {{Henksmeier, Tobias and Eppinger, Martin and Reineke, Bernhard and Zentgraf, Thomas and Meier, Cedrik and Reuter, Dirk}},
  journal      = {{physica status solidi (a)}},
  keywords     = {{epitaxial lift-off, GaAs/AlxGa1−xAs heterostructures, selective etching}},
  number       = {{3}},
  pages        = {{2000408}},
  title        = {{{Selective Etching of (111)B-Oriented AlxGa1−xAs-Layers for Epitaxial Lift-Off}}},
  doi          = {{https://doi.org/10.1002/pssa.202000408}},
  volume       = {{218}},
  year         = {{2021}},
}

@inproceedings{20693,
  abstract     = {{In practical, large-scale networks, services are requested
by users across the globe, e.g., for video streaming.
Services consist of multiple interconnected components such as
microservices in a service mesh. Coordinating these services
requires scaling them according to continuously changing user
demand, deploying instances at the edge close to their users,
and routing traffic efficiently between users and connected instances.
Network and service coordination is commonly addressed
through centralized approaches, where a single coordinator
knows everything and coordinates the entire network globally.
While such centralized approaches can reach global optima, they
do not scale to large, realistic networks. In contrast, distributed
approaches scale well, but sacrifice solution quality due to their
limited scope of knowledge and coordination decisions.

To this end, we propose a hierarchical coordination approach
that combines the good solution quality of centralized approaches
with the scalability of distributed approaches. In doing so, we divide
the network into multiple hierarchical domains and optimize
coordination in a top-down manner. We compare our hierarchical
with a centralized approach in an extensive evaluation on a real-world
network topology. Our results indicate that hierarchical
coordination can find close-to-optimal solutions in a fraction of
the runtime of centralized approaches.}},
  author       = {{Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}},
  booktitle    = {{IFIP/IEEE International Symposium on Integrated Network Management (IM)}},
  keywords     = {{network management, service management, coordination, hierarchical, scalability, nfv}},
  location     = {{Bordeaux, France}},
  publisher    = {{IFIP/IEEE}},
  title        = {{{Divide and Conquer: Hierarchical Network and Service Coordination}}},
  year         = {{2021}},
}

@inproceedings{22155,
  author       = {{Gottschalk, Sebastian}},
  booktitle    = {{Advanced Software Engineering. Doctorial Consortium}},
  publisher    = {{CEUR}},
  title        = {{{Situation-specific Development of Business Models for Services in Software Ecosystems}}},
  year         = {{2021}},
}

@inproceedings{22156,
  abstract     = {{Word embedding models reflect bias towards genders, ethnicities, and other social groups present in the underlying training data. Metrics such as ECT, RNSB, and WEAT quantify bias in these models based on predefined word lists representing social groups and bias-conveying concepts. How suitable these lists actually are to reveal bias - let alone the bias metrics in general - remains unclear, though. In this paper, we study how to assess the quality of bias metrics for word embedding models. In particular, we present a generic method, Bias Silhouette Analysis (BSA), that quantifies the accuracy and robustness of such a metric and of the word lists used. Given a biased and an unbiased reference embedding model, BSA applies the metric systematically for several subsets of the lists to the models. The variance and rate of convergence of the bias values of each model then entail the robustness of the word lists, whereas the distance between the models' values gives indications of the general accuracy of the metric with the word lists. We demonstrate the behavior of BSA on two standard embedding models for the three mentioned metrics with several word lists from existing research.}},
  author       = {{Spliethöver, Maximilian and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21}},
  location     = {{Online}},
  pages        = {{552--559}},
  title        = {{{Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models}}},
  doi          = {{10.24963/ijcai.2021/77}},
  year         = {{2021}},
}

@inproceedings{22158,
  author       = {{Syed, Shahbaz and Al-Khatib, Khalid and Alshomary, Milad and Wachsmuth, Henning and Potthast, Martin}},
  booktitle    = {{Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021): Findings}},
  pages        = {{3482--3493}},
  title        = {{{Generating Informative Conclusions for Argumentative Texts}}},
  year         = {{2021}},
}

@inproceedings{22159,
  author       = {{Barrow, Joe and Jain, Rajiv and Lipka, Nedim and Dernoncourt, Franck and Morariu, Vlad and Manjunatha, Varun and Oard, Douglas and Resnik, Philip and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)}},
  pages        = {{1583--1595}},
  title        = {{{Syntopical Graphs for Computational Argumentation Tasks}}},
  year         = {{2021}},
}

@misc{22304,
  author       = {{Schott, Stefan}},
  title        = {{{Android App Analysis Benchmark Case Generation}}},
  year         = {{2021}},
}

@article{22450,
  abstract     = {{We realize and investigate a nonlinear metasurface taking advantage of intersubband transitions in ultranarrow GaN/AlN multi-quantum well heterostructures. Owing to huge band offsets, the structures offer resonant transitions in the telecom window around 1.55 µm. These heterostructures are functionalized with an array of plasmonic antennas featuring cross-polarized resonances at these near-infrared wavelengths and their second harmonic. This kind of nonlinear metasurface allows for substantial second-harmonic generation at normal incidence which is completely absent for an antenna array without the multi-quantum well structure underneath. While the second harmonic is originally radiated only into the plane of the quantum wells, a proper geometrical arrangement of the plasmonic elements permits the redirection of the second-harmonic light to free-space radiation, which is emitted perpendicular to the surface.}},
  author       = {{Mundry, Jan and Spreyer, Florian and Jmerik, Valentin and Ivanov, Sergey and Zentgraf, Thomas and Betz, Markus}},
  issn         = {{2159-3930}},
  journal      = {{Optical Materials Express}},
  number       = {{7}},
  publisher    = {{OSA}},
  title        = {{{Nonlinear metasurface combining telecom-range intersubband transitions in GaN/AlN quantum wells with resonant plasmonic antenna arrays}}},
  doi          = {{10.1364/ome.426236}},
  volume       = {{11}},
  year         = {{2021}},
}

@unpublished{22509,
  abstract     = {{Self-training is an effective approach to semi-supervised learning. The key idea is to let the learner itself iteratively generate "pseudo-supervision" for unlabeled instances based on its current hypothesis. In combination with consistency regularization, pseudo-labeling has shown promising performance in various domains, for example in computer vision. To account for the hypothetical nature of the pseudo-labels, these are commonly provided in the form of probability distributions. Still, one may argue that even a probability distribution represents an excessive level of informedness, as it suggests that the learner precisely knows the ground-truth conditional probabilities. In our approach, we therefore allow the learner to label instances in the form of credal sets, that is, sets of (candidate) probability distributions. Thanks to this increased expressiveness, the learner is able to represent uncertainty and a lack of knowledge in a more flexible and more faithful manner. To learn from weakly labeled data of that kind, we leverage methods that have recently been proposed in the realm of so-called superset learning. In an exhaustive empirical evaluation, we compare our methodology to state-of-the-art self-supervision approaches, showing competitive to superior performance especially in low-label scenarios incorporating a high degree of uncertainty.}},
  author       = {{Lienen, Julian and Hüllermeier, Eyke}},
  booktitle    = {{arXiv:2106.11853}},
  title        = {{{Credal Self-Supervised Learning}}},
  year         = {{2021}},
}

@inproceedings{22514,
  author       = {{Kucklick, Jan-Peter and Müller, Jennifer and Beverungen, Daniel and Müller, Oliver}},
  booktitle    = {{European Conference on Information Systems}},
  location     = {{Virtual}},
  title        = {{{Quantifying the Impact of Location Data for Real Estate Appraisal – A GIS-based Deep Learning Approach}}},
  year         = {{2021}},
}