@inproceedings{25278,
  abstract     = {{Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia. It relies on allocating network resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm, so that it optimizes the performance of the SFC. When the load of incoming requests -- competing for the limited network resources -- increases, it becomes challenging to decide which requests should be admitted and which one should be rejected. In this work, we propose a deep Reinforcement learning (RL) solution that can learn the admission policy for different dependencies, such as the service lifetime and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve baseline that admits a request whenever there are available resources. We show that deep RL outperforms the baseline and provides higher acceptance rate with low rejections even when there are enough resources.}},
  author       = {{Afifi, Haitham and Sauer, Fabian Jakob and Karl, Holger}},
  booktitle    = {{2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS'21)}},
  keywords     = {{reinforcement learning, admission control, wireless sensor networks}},
  title        = {{{Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding}}},
  year         = {{2021}},
}

@inproceedings{25281,
  abstract     = {{Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal processing applications. Due to the spatial diversity of the microphone and their relative position to the acoustic source, not all microphones are equally useful for subsequent audio signal processing tasks, nor do they all have the same wireless data transmission rates. Hence, a central task in WASNs is to balance a microphone’s estimated acoustic utility against its transmission delay, selecting a best-possible subset of microphones to record audio signals.

In this work, we use reinforcement learning to decide if a microphone should be used or switched off to maximize the acoustic quality at low transmission delays, while minimizing switching frequency. In experiments with moving sources in a simulated acoustic environment, our method outperforms naive baseline comparisons}},
  author       = {{Afifi, Haitham and Guenther, Michael and Brendel, Andreas and Karl, Holger and Kellermann, Walter}},
  booktitle    = {{14. ITG Conference on Speech Communication (ITG 2021)}},
  keywords     = {{microphone utility, microphone selection, wireless acoustic sensor network, network delay, reinforcement learning}},
  title        = {{{Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities}}},
  year         = {{2021}},
}

@inproceedings{25293,
  author       = {{Gunther, Michael and Afifi, Haitham and Brendel, Andreas and Karl, Holger and Kellermann, Walter}},
  booktitle    = {{ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  title        = {{{Network-Aware Optimal Microphone Channel Selection in Wireless Acoustic Sensor Networks}}},
  doi          = {{10.1109/icassp39728.2021.9414528}},
  year         = {{2021}},
}

@inproceedings{25331,
  author       = {{Brinkmann, Marcus and Dresen, Christian and Merget, Robert and Poddebniak, Damian and Müller, Jens and Somorovsky, Juraj and Schwenk, Jörg and Schinzel, Sebastian}},
  booktitle    = {{30th {USENIX} Security Symposium ({USENIX} Security 21)}},
  isbn         = {{978-1-939133-24-3}},
  pages        = {{4293--4310}},
  publisher    = {{{USENIX} Association}},
  title        = {{{ALPACA: Application Layer Protocol Confusion - Analyzing and Mitigating Cracks in TLS Authentication}}},
  year         = {{2021}},
}

@inproceedings{25332,
  author       = {{Merget, Robert and Brinkmann, Marcus and Aviram, Nimrod and Somorovsky, Juraj and Mittmann, Johannes and Schwenk, Jörg}},
  booktitle    = {{30th {USENIX} Security Symposium ({USENIX} Security 21)}},
  isbn         = {{978-1-939133-24-3}},
  pages        = {{213--230}},
  publisher    = {{{USENIX} Association}},
  title        = {{{Raccoon Attack: Finding and Exploiting Most-Significant-Bit-Oracles in TLS-DH(E)}}},
  year         = {{2021}},
}

@inbook{25448,
  author       = {{Heggemann, Thomas and Sapli, Hüseyin and Homberg, W.}},
  booktitle    = {{Forming the Future}},
  issn         = {{2367-1181}},
  title        = {{{Experimental and Numerical Investigations into the Influence of the Process Parameters During the Deep Drawing of Fiber Metal Laminates}}},
  doi          = {{10.1007/978-3-030-75381-8_219}},
  year         = {{2021}},
}

@inproceedings{25518,
  author       = {{Stüker, Daniel and Schöppner, Volker}},
  location     = {{Montreal}},
  title        = {{{Simplified Numerical Calculation of the Isothermal, Three-Dimensional, Non-Newtonian Flow Characteristics of Single-Screw Melt-Extruders}}},
  year         = {{2021}},
}

@inproceedings{25519,
  author       = {{Stüker, Daniel and Schöppner, Volker}},
  location     = {{Montreal}},
  title        = {{{Non-Isothermal Calculation of the Pressure-Throughput-Characteristics of Single Screw Melt-Extruders}}},
  year         = {{2021}},
}

@proceedings{25521,
  editor       = {{Schulte, Carsten and A. Becker, Brett and Divitini, Monica and Barendsen, Erik}},
  isbn         = {{978-1-4503-8397-4}},
  publisher    = {{ACM}},
  title        = {{{ITiCSE 2021: 26th ACM Conference on Innovation and Technology in Computer Science Education, Virtual Event, Germany, June 26 - July 1, 2021 - Working Group Reports}}},
  doi          = {{10.1145/3456565}},
  year         = {{2021}},
}

@proceedings{25522,
  editor       = {{Schulte, Carsten and A. Becker, Brett and Divitini, Monica and Barendsen, Erik}},
  isbn         = {{978-1-4503-8214-4}},
  publisher    = {{ACM}},
  title        = {{{ITiCSE 2021: 26th ACM Conference on Innovation and Technology in Computer Science Education, Virtual Event, Germany, June 26 - July 1, 2021}}},
  doi          = {{10.1145/3430665}},
  year         = {{2021}},
}

@inproceedings{25525,
  author       = {{Große-Bölting, Gregor and Gerstenberger, Dietrich Karl-Heinz and Gildehaus, Lara and Mühling, Andreas and Schulte, Carsten}},
  booktitle    = {{ICER 2021: ACM Conference on International Computing Education Research, Virtual Event, USA, August 16-19, 2021}},
  editor       = {{J. Ko, Amy and Vahrenhold, Jan and McCauley, René and Hauswirth, Matthias}},
  pages        = {{169--183}},
  publisher    = {{ACM}},
  title        = {{{Identity in K-12 Computer Education Research: A Systematic Literature Review}}},
  doi          = {{10.1145/3446871.3469757}},
  year         = {{2021}},
}

@article{25527,
  author       = {{Schulte, Carsten and A. Becker, Brett}},
  journal      = {{ACM SIGCSE Bull.}},
  number       = {{3}},
  pages        = {{3--4}},
  title        = {{{ITiCSE 2021 recap}}},
  doi          = {{10.1145/3483403.3483405}},
  volume       = {{53}},
  year         = {{2021}},
}

@inproceedings{25576,
  author       = {{Moritzer, Elmar and Krassmann, Dimitri and Brikmann, Johannes}},
  title        = {{{Joining of Sheet Metal and Thermoplastic Composites Using Injection Riveting}}},
  year         = {{2021}},
}

@article{25577,
  author       = {{Moritzer, Elmar and Krassmann, Dimitri and Brikmann, Johannes}},
  journal      = {{Joining Plastics}},
  number       = {{3-4}},
  title        = {{{Fügen von thermoplastischen Composites mit Metallteilen durch Spritznieten}}},
  volume       = {{15}},
  year         = {{2021}},
}

@article{25605,
  abstract     = {{The nonlinear process of second harmonic generation (SHG) in monolayer (1L) transition metal dichalcogenides (TMD), like WS2, strongly depends on the polarization state of the excitation light. By combination of plasmonic nanostructures with 1L-WS2 by transferring it onto a plasmonic nanoantenna array, a hybrid metasurface is realized impacting the polarization dependency of its SHG. Here, we investigate how plasmonic dipole resonances affect the process of SHG in plasmonic–TMD hybrid metasurfaces by nonlinear spectroscopy. We show that the polarization dependency is affected by the lattice structure of plasmonic nanoantenna arrays as well as by the relative orientation between the 1L-WS2 and the individual plasmonic nanoantennas. In addition, such hybrid metasurfaces show SHG in polarization states, where SHG is usually forbidden for either 1L-WS2 or plasmonic nanoantennas. By comparing the SHG in these channels with the SHG generated by the hybrid metasurface components, we detect an enhancement of the SHG signal by a factor of more than 40. Meanwhile, an attenuation of the SHG signal in usually allowed polarization states is observed. Our study provides valuable insight into hybrid systems where symmetries strongly affect the SHG and enable tailored SHG in 1L-WS2 for future applications.}},
  author       = {{Spreyer, Florian and Ruppert, Claudia and Georgi, Philip and Zentgraf, Thomas}},
  issn         = {{1936-0851}},
  journal      = {{ACS Nano}},
  number       = {{10}},
  pages        = {{16719--16728}},
  title        = {{{Influence of Plasmon Resonances and Symmetry Effects on Second Harmonic Generation in WS2–Plasmonic Hybrid Metasurfaces}}},
  doi          = {{10.1021/acsnano.1c06693}},
  volume       = {{15}},
  year         = {{2021}},
}

@inproceedings{20115,
  author       = {{Skitalinskaya, Gabriella and Klaff, Jonas and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics}},
  pages        = {{1718--1729}},
  title        = {{{Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale}}},
  year         = {{2021}},
}

@inproceedings{20125,
  abstract     = {{Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load.}},
  author       = {{Hasnain, Asif and Karl, Holger}},
  booktitle    = {{2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)}},
  keywords     = {{Flow scheduling, Deadlines, Reinforcement learning}},
  location     = {{Las Vegas, USA}},
  publisher    = {{IEEE Computer Society}},
  title        = {{{Learning Flow Scheduling}}},
  doi          = {{https://doi.org/10.1109/CCNC49032.2021.9369514}},
  year         = {{2021}},
}

@inproceedings{20244,
  author       = {{Gottschalk, Sebastian and Kirchhoff, Jonas and Engels, Gregor}},
  booktitle    = {{Business Modeling and Software Design}},
  editor       = {{Shishkov, Boris}},
  location     = {{Sofia}},
  title        = {{{Extending Business Model Development Tools with Consolidated Expert Knowledge }}},
  doi          = {{10.1007/978-3-030-79976-2_1}},
  year         = {{2021}},
}

@article{27970,
  author       = {{Barclay, AW and LSA, Augustin and Brighenti, F and Delport, E and Henry, CJ and Sievenpiper, JL and Usic, K and Yuexin, Y and Zurbau, A and TMS, Wolever and Astrup, A and Bulló, M and Buyken, Anette and Ceriello, A and Ellis, PR and Vanginkel, MA and CWC, Kendall and La Vecchia, C and Livesey, G and Poli, A and Riccardi, G and Salas-Salvadó, J and Trichopoulou, A and Bhaskaran, K and DJA, Jenkins and Willett, WC and Brand-Miller, JC}},
  issn         = {{2072-6643}},
  journal      = {{Nutrients}},
  number       = {{9}},
  title        = {{{Dietary Glycaemic Index Labelling: A Global Perspective.}}},
  doi          = {{10.3390/nu13093244}},
  volume       = {{13}},
  year         = {{2021}},
}

@article{27995,
  abstract     = {{<jats:title>Zusammenfassung</jats:title><jats:p>Studien zum Übergang von der Grund- in die weiterführende Schule gibt es zu zahlreichen Themen. Wie Grundschulen den Übergang zur weiterführenden Schule gestalten, wurde bislang jedoch kaum erforscht. Auf Basis des Forschungsstands werden relevante Maßnahmen identifiziert. In einer online-Befragung an 106 Grundschulen in Nordrhein-Westfalen wurde erfasst, inwiefern diese Maßnahmen tatsächlich umgesetzt werden und als wie relevant bzw. schwierig ihre Umsetzung erachtet wird. Die Ergebnisse werden verglichen mit Daten einer Studie, die 2002 an 71 Grundschulen durchgeführt wurde. Es zeigt sich eine hohe Stabilität der Gestaltung in den letzten 15 Jahren. Maßnahmen, die auf stufenübergreifender Kooperation basieren, werden heute zwar häufiger praktiziert, stehen aber weiterhin am Ende der Rangreihe.</jats:p>}},
  author       = {{van Ophuysen, Stefanie and Schürer, Sina and Bloh, Bea}},
  issn         = {{1865-3553}},
  journal      = {{Zeitschrift für Grundschulforschung}},
  pages        = {{149--167}},
  title        = {{{Die Gestaltung des Übergangs zur Weiterführenden Schule – Welche Maßnahmen wurden und werden an Grundschulen in NRW praktiziert?}}},
  doi          = {{10.1007/s42278-020-00101-8}},
  year         = {{2021}},
}