@article{25213,
  author       = {{Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille and Wehrheim, Heike}},
  journal      = {{CoRR}},
  title        = {{{MLCheck- Property-Driven Testing of Machine Learning Models}}},
  volume       = {{abs/2105.00741}},
  year         = {{2021}},
}

@article{25214,
  author       = {{Wilke, Adrian and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{CoRR}},
  title        = {{{Open Data Portal Germany (OPAL) Projektergebnisse}}},
  volume       = {{abs/2105.03161}},
  year         = {{2021}},
}

@article{25215,
  author       = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{CoRR}},
  title        = {{{Out-of-Vocabulary Entities in Link Prediction}}},
  volume       = {{abs/2105.12524}},
  year         = {{2021}},
}

@article{25217,
  author       = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  journal      = {{CoRR}},
  title        = {{{DRILL- Deep Reinforcement Learning for Refinement Operators in ALC}}},
  volume       = {{abs/2106.15373}},
  year         = {{2021}},
}

@inproceedings{25242,
  author       = {{Laux, Florian and Görzen, Thomas}},
  booktitle    = {{Proceedings of the 42nd International Conference on Information Systems (ICIS)}},
  title        = {{{Trust Me, I’m Confident – Are Confident Members of the Crowd Better at Evaluating Business Model Ideas?}}},
  year         = {{2021}},
}

@article{25272,
  author       = {{Engelkemeier, Katja and Sun, Aijia and Voswinkel, Dietrich and Grydin, Olexandr and Schaper, Mirko and Bremser, Wolfgang}},
  issn         = {{2196-0216}},
  journal      = {{ChemElectroChem}},
  pages        = {{2155--2168}},
  title        = {{{Zinc Anodizing: Structural Diversity of Anodic Zinc Oxide Controlled by the Type of Electrolyte}}},
  doi          = {{10.1002/celc.202100216}},
  year         = {{2021}},
}

@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}},
}