@inproceedings{15372,
  author       = {{Nuriddinov, Askhat and Tavernier, Wouter and Colle, Didier and Pickavet, Mario and Peuster, Manuel and Schneider, Stefan Balthasar}},
  booktitle    = {{ IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}},
  publisher    = {{IEEE}},
  title        = {{{Reproducible Functional Tests for Multi-scale Network Services}}},
  year         = {{2019}},
}

@inproceedings{15373,
  abstract     = {{Offloading packet processing tasks to programmable switches and/or to programmable network interfaces, so called “SmartNICs”, is one of the key concepts to prepare softwarized networks for the high traffic demands of the future. However, implementing network functions that make use of those offload- ing technologies is still challenging and usually requires the availability of specialized hardware. It becomes even harder if heterogeneous services, making use of different offloading and network virtualization technologies, should be developed.
In this paper, we introduce FOP4 (Function Offloading Pro- totyping with P4), a novel prototyping platform that allows to prototype heterogeneous software network scenarios, including container-based, P4-switch-based, and SmartNIC-based network functions. The presented work substantially extends our existing Containernet platform with the means to prototype offloading scenarios. Besides presenting the platform’s system design, we evaluate its scalability and show that it can run scenarios with more than 64 P4 switch or SmartNIC nodes on a single laptop. Finally, we presented a case study in which we use the presented platform to prototype an extended in-band network telemetry use case.}},
  author       = {{Moro, Daniele and Peuster, Manuel and Karl, Holger and Capone, Antonio}},
  booktitle    = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}},
  publisher    = {{IEEE}},
  title        = {{{FOP4: Function Offloading Prototyping in Heterogeneous and Programmable Network Scenarios}}},
  year         = {{2019}},
}

@inproceedings{15374,
  abstract     = {{Emulation platforms supporting Virtual Network Functions (VNFs) allow developers to rapidly prototype network services. None of the available platforms, however, supports experimenting with programmable data planes to enable VNF offloading. In this demonstration, we show FOP4, a flexible platform that provides support for Docker-based VNFs, and VNF offloading, by means of P4-enabled switches. The platform provides interfaces to program the P4 devices and to deploy network functions. We demonstrate FOP4 with two complex example scenarios, demonstrating how developers can exploit data plane programmability to implement network functions.}},
  author       = {{Moro, Daniele and Peuster, Manuel and Karl, Holger and Capone, Antonio}},
  booktitle    = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}},
  publisher    = {{IEEE}},
  title        = {{{Demonstrating FOP4: A Flexible Platform to Prototype NFV Offloading Scenarios}}},
  year         = {{2019}},
}

@inproceedings{15375,
  author       = {{Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}},
  booktitle    = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}},
  publisher    = {{IEEE}},
  title        = {{{Putting NFV into Reality: Physical Smart Manufacturing Testbed}}},
  year         = {{2019}},
}

@inproceedings{15376,
  author       = {{Behnke, Daniel and Müller, Marcel and Bök, Patrick-Benjamin and Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}},
  booktitle    = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}},
  publisher    = {{IEEE}},
  title        = {{{NFV-driven intrusion detection for smart manufacturing}}},
  year         = {{2019}},
}

@inproceedings{15422,
  author       = {{Ho, Nam and Kaufmann, Paul and Platzner, Marco}},
  booktitle    = {{World Congress on Nature and Biologically Inspired Computing (NaBIC)}},
  publisher    = {{Springer}},
  title        = {{{Optimization of Application-specific L1 Cache Translation Functions of the LEON3 Processor}}},
  year         = {{2019}},
}

@phdthesis{15430,
  author       = {{Yigitbas, Enes}},
  title        = {{{Model-Driven Engineering of Self-Adaptive User Interfaces}}},
  year         = {{2019}},
}

@inproceedings{15478,
  abstract     = {{Stratix 10 FPGA cards have a good potential for the acceleration of HPC workloads since the Stratix 10 product line introduces devices with a large number of DSP and memory blocks. The high level synthesis of OpenCL codes can play a fundamental role for FPGAs in HPC, because it allows to implement different designs with lower development effort compared to hand optimized HDL. However, Stratix 10 cards are still hard to fully exploit using the Intel FPGA SDK for OpenCL. The implementation of designs with thousands of concurrent arithmetic operations often suffers from place and route problems that limit the maximum frequency or entirely prevent a successful synthesis. In order to overcome these issues for the implementation of the matrix multiplication, we formulate Cannon's matrix multiplication algorithm with regard to its efficient synthesis within the FPGA logic. We obtain a two-level block algorithm, where the lower level sub-matrices are multiplied using our Cannon's algorithm implementation. Following this design approach with multiple compute units, we are able to get maximum frequencies close to and above 300 MHz with high utilization of DSP and memory blocks. This allows for performance results above 1 TeraFLOPS.}},
  author       = {{Gorlani, Paolo and Kenter, Tobias and Plessl, Christian}},
  booktitle    = {{Proceedings of the International Conference on Field-Programmable Technology (FPT)}},
  publisher    = {{IEEE}},
  title        = {{{OpenCL Implementation of Cannon's Matrix Multiplication Algorithm on Intel Stratix 10 FPGAs}}},
  doi          = {{10.1109/ICFPT47387.2019.00020}},
  year         = {{2019}},
}

@inproceedings{15578,
  author       = {{Izu, Cruz and Schulte, Carsten and Aggarwal, Ashish and I. Cutts, Quintin and Duran, Rodrigo and Gutica, Mirela and Heinemann, Birte and Kraemer, Eileen and Lonati, Violetta and Mirolo, Claudio and Weeda, Renske}},
  booktitle    = {{Proceedings of the 2019 (ACM) Conference on Innovation and Technology in Computer Science Education, Aberdeen, Scotland, UK, July 15-17, 2019}},
  pages        = {{261--262}},
  title        = {{{Program Comprehension: Identifying Learning Trajectories for Novice Programmers}}},
  doi          = {{10.1145/3304221.3325531}},
  year         = {{2019}},
}

@inproceedings{15579,
  author       = {{Kapp, Florian and Schulte, Carsten}},
  booktitle    = {{Informatik für alle, 18. GI-Fachtagung Informatik und Schule, (INFOS) 2019, 16.-18. September 2019, Dortmund}},
  pages        = {{247--256}},
  title        = {{{Einsatz von Jupyter Notebooks am Beispiel eines fiktiven Kriminalfalls}}},
  doi          = {{10.18420/infos2019-c10}},
  year         = {{2019}},
}

@inproceedings{15581,
  author       = {{Müller, Kathrin and Schulte, Carsten and Magenheim, Johannes}},
  booktitle    = {{Informatik für alle, 18. GI-Fachtagung Informatik und Schule, (INFOS) 2019, 16.-18. September 2019, Dortmund}},
  pages        = {{139--148}},
  title        = {{{Zur Relevanz eines Prozessbereiches Interaktion und Exploration im Kontext informatischer Bildung im Primarbereich}}},
  doi          = {{10.18420/infos2019-b10}},
  year         = {{2019}},
}

@inproceedings{15583,
  author       = {{Schmidt, Ann-Katrin and Schulte, Carsten}},
  booktitle    = {{Informatik für alle, 18. GI-Fachtagung Informatik und Schule, (INFOS) 2019, 16.-18. September 2019, Dortmund}},
  pages        = {{315--324}},
  title        = {{{Das RetiBNE Café}}},
  doi          = {{10.18420/infos2019-c17}},
  year         = {{2019}},
}

@inproceedings{15720,
  author       = {{Wilke, Adrian and Magenheim, Johannes}},
  booktitle    = {{IEEE Global Engineering Education Conference, EDUCON 2019, Dubai, United Arab Emirates, April 8-11, 2019}},
  pages        = {{892--899}},
  title        = {{{Critical Incidents for Technology Enhanced Learning in Vocational Education and Training}}},
  doi          = {{10.1109/EDUCON.2019.8725025}},
  year         = {{2019}},
}

@book{15721,
  author       = {{Köller, Olaf and Magenheim, Johannes and Molitor, Heike and Pfenning, Uwe and Ramseger, J{\ and Steffensky, Mirjam and Wiesmüller, Christian and Winther, Esther and Wollring, Bernd}},
  publisher    = {{Verlag Barbara Budrich}},
  title        = {{{Zieldimensionen für Multiplikatorinnen und Multiplikatoren früher MINT-Bildung}}},
  year         = {{2019}},
}

@article{15741,
  abstract     = {{
In many cyber–physical systems, we encounter the problem of remote state estimation of geo- graphically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors has to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant one needs to decide which sensors have network access and which ones do not. To address this scheduling problem, we formulate an associated Markov decision process (MDP). This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others. Our algorithm is shown to significantly outperform these algorithms for many example scenario}},
  author       = {{Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}},
  issn         = {{0005-1098}},
  journal      = {{Automatica}},
  title        = {{{Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}}},
  doi          = {{10.1016/j.automatica.2019.108759}},
  year         = {{2019}},
}

@misc{15746,
  author       = {{Otte, Oliver}},
  title        = {{{Outsourced Decryption of Attribute-based Ciphertexts}}},
  year         = {{2019}},
}

@misc{15747,
  author       = {{Wördenweber, Nico Christof}},
  title        = {{{On the Security of the Rouselakis-Waters Ciphertext-Policy Attribute-Based Encryption Scheme in the Random Oracle Model}}},
  year         = {{2019}},
}

@misc{15819,
  author       = {{Leutnant, Matthias}},
  title        = {{{Experimentelle Untersuchung des SEM-Algorithmus}}},
  year         = {{2019}},
}

@inproceedings{15838,
  abstract     = {{In the field of software analysis a trade-off between scalability and accuracy always exists. In this respect, Android app analysis is no exception, in particular, analyzing large or many apps can be challenging. Dealing with many small apps is a typical challenge when facing micro-benchmarks such as DROIDBENCH or ICC-BENCH. These particular benchmarks are not only used for the evaluation of novel tools but also in continuous integration pipelines of existing mature tools to maintain and guarantee a certain quality-level. Considering this latter usage it becomes very important to be able to achieve benchmark results as fast as possible. Hence, benchmarks have to be optimized for this purpose. One approach to do so is app merging. We implemented the Android Merge Tool (AMT) following this approach and show that its novel aspects can be used to produce scaled up and accurate benchmarks. For such benchmarks Android app analysis tools do not suffer from the scalability-accuracy trade-off anymore. We show this throughout detailed experiments on DROIDBENCH employing three different analysis tools (AMANDROID, ICCTA, FLOWDROID). Benchmark execution times are largely reduced without losing benchmark accuracy. Moreover, we argue why AMT is an advantageous successor of the state-of-the-art app merging tool (APKCOMBINER) in analysis lift-up scenarios.}},
  author       = {{Pauck, Felix and Zhang, Shikun}},
  booktitle    = {{2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW)}},
  isbn         = {{9781728141367}},
  keywords     = {{Program Analysis, Android App Analysis, Taint Analysis, App Merging, Benchmark}},
  title        = {{{Android App Merging for Benchmark Speed-Up and Analysis Lift-Up}}},
  doi          = {{10.1109/asew.2019.00019}},
  year         = {{2019}},
}

@misc{15883,
  author       = {{Kumar Jeyakumar, Shankar}},
  title        = {{{Incremental learning with Support Vector Machine on embedded platforms}}},
  year         = {{2019}},
}