@misc{701, author = {{Götte, Thorsten}}, publisher = {{Universität Paderborn}}, title = {{{Self-Stabilizing Spanners for Tree Metrics}}}, year = {{2017}}, } @article{7011, author = {{Hottung, André and Tanaka, Shunji and Tierney, Kevin}}, journal = {{CoRR abs/1709.09972}}, title = {{{Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem}}}, year = {{2017}}, } @article{7012, author = {{Fazal-Baqaie, Masud and Güldali, Baris and Oberthür, Simon}}, journal = {{CSE@SE 2017}}, pages = {{18--21}}, title = {{{Towards DevOps in Multi-provider Projects}}}, year = {{2017}}, } @article{7020, author = {{Ritzmann, Julian and Schott, Rüdiger and Gross, Katherine and Reuter, Dirk and Ludwig, Arne and Wieck, Andreas D.}}, issn = {{0022-0248}}, journal = {{Journal of Crystal Growth}}, pages = {{7--10}}, publisher = {{Elsevier BV}}, title = {{{Overcoming Ehrlich-Schwöbel barrier in (1 1 1)A GaAs molecular beam epitaxy}}}, doi = {{10.1016/j.jcrysgro.2017.10.029}}, volume = {{481}}, year = {{2017}}, } @article{7026, author = {{Zolatanosha, Viktoryia and Reuter, Dirk}}, issn = {{0167-9317}}, journal = {{Microelectronic Engineering}}, pages = {{35--39}}, publisher = {{Elsevier BV}}, title = {{{Robust Si 3 N 4 masks for 100 nm selective area epitaxy of GaAs-based nanostructures}}}, doi = {{10.1016/j.mee.2017.05.053}}, volume = {{180}}, year = {{2017}}, } @article{7027, author = {{Scholz, Sven and Schott, Rüdiger and Labud, Patrick A. and Somsen, Christoph and Reuter, Dirk and Ludwig, Arne and Wieck, Andreas D.}}, issn = {{0022-0248}}, journal = {{Journal of Crystal Growth}}, pages = {{46--50}}, publisher = {{Elsevier BV}}, title = {{{Focused ion beam supported growth of monocrystalline wurtzite InAs nanowires grown by molecular beam epitaxy}}}, doi = {{10.1016/j.jcrysgro.2017.04.013}}, volume = {{470}}, year = {{2017}}, } @article{7028, author = {{Kuznetsova, M. S. and Cherbunin, R. V. and Gerlovin, I. Ya. and Ignatiev, I. V. and Verbin, S. Yu. and Yakovlev, D. R. and Reuter, Dirk and Wieck, A. D. and Bayer, M.}}, issn = {{2469-9950}}, journal = {{Physical Review B}}, number = {{15}}, publisher = {{American Physical Society (APS)}}, title = {{{Spin dynamics of quadrupole nuclei in InGaAs quantum dots}}}, doi = {{10.1103/physrevb.95.155312}}, volume = {{95}}, year = {{2017}}, } @article{7029, author = {{Srinivasan, A. and Miserev, D. S. and Hudson, K. L. and Klochan, O. and Muraki, K. and Hirayama, Y. and Reuter, Dirk and Wieck, A. D. and Sushkov, O. P. and Hamilton, A. R.}}, issn = {{0031-9007}}, journal = {{Physical Review Letters}}, number = {{14}}, publisher = {{American Physical Society (APS)}}, title = {{{Detection and Control of Spin-Orbit Interactions in a GaAs Hole Quantum Point Contact}}}, doi = {{10.1103/physrevlett.118.146801}}, volume = {{118}}, year = {{2017}}, } @phdthesis{703, author = {{Podlipyan, Pavel}}, publisher = {{Universität Paderborn}}, title = {{{Local Algorithms for the Continuous Gathering Problem}}}, doi = {{10.17619/UNIPB/1-230}}, year = {{2017}}, } @phdthesis{704, author = {{Riechers, Sören}}, publisher = {{Universität Paderborn}}, title = {{{Scheduling with Scarce Resources}}}, doi = {{10.17619/UNIPB/1-231}}, year = {{2017}}, } @article{706, author = {{Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören}}, journal = {{Journal of Combinatorial Optimization}}, number = {{4}}, pages = {{1168--1194}}, publisher = {{Springer}}, title = {{{Cost-efficient Scheduling on Machines from the Cloud}}}, doi = {{10.1007/s10878-017-0198-x}}, volume = {{36}}, year = {{2017}}, } @phdthesis{707, author = {{Walther, Sven}}, publisher = {{Universität Paderborn}}, title = {{{Knowledge-based Verification of Service Compositions}}}, doi = {{10.17619/UNIPB/1-307}}, year = {{2017}}, } @inproceedings{708, author = {{Schwabe, Arne and Rojas, Elisa and Karl, Holger}}, booktitle = {{2017 {IEEE} Conference on Network Softwarization, NetSoft 2017, Bologna, Italy, July 3-7, 2017}}, location = {{Bologna}}, pages = {{1----5}}, title = {{{Minimizing downtimes: Using dynamic reconfiguration and state management in SDN}}}, doi = {{10.1109/NETSOFT.2017.8004209}}, year = {{2017}}, } @inproceedings{709, author = {{Laux, Sven and Pannu, Gurjashan Singh and Schneider, Stefan Balthasar and Tiemann, Jan and Klingler, Florian and Sommer, Christoph and Dressler, Falko}}, booktitle = {{2016 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781509051977}}, publisher = {{IEEE}}, title = {{{Demo: OpenC2X — An open source experimental and prototyping platform supporting ETSI ITS-G5}}}, doi = {{10.1109/vnc.2016.7835955}}, year = {{2017}}, } @inproceedings{71, abstract = {{Today, software verification tools have reached the maturity to be used for large scale programs. Different tools perform differently well on varying code. A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice. Such rankings can, however, so far only be obtained by running all considered tools on the program.In this paper, we present a machine learning approach to predicting rankings of tools on programs. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for programs. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6).}}, author = {{Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, booktitle = {{Proceedings of the 3rd International Workshop on Software Analytics}}, pages = {{23--26}}, title = {{{Predicting Rankings of Software Verification Tools}}}, doi = {{10.1145/3121257.3121262}}, year = {{2017}}, } @inproceedings{717, abstract = {{In conventional large-scale networks, creation and management of network services are costly and complex tasks that often consume a lot of resources, including time and manpower. Network softwarization and network function virtualization have been introduced to tackle these problems, aiming at decreasing costs and complexity of implementing new services, maintaining the implemented services, and managing available resources in service provisioning platforms and underlying infrastructures. To experience the full potential of these approaches, innovative development support tools and service provisioning environments are needed. To answer these needs, we introduce the architecture of the open-source SONATA system, a service programming, orchestration, and management framework. We present a development toolchain for virtualized network services, fully integrated with a service platform and orchestration system. We introduce the modular and flexible architecture of our system and discuss its main components and features, such as function- and service-specific managers that allow fine-grained service management, slicing support to facilitate multi-tenancy, recursiveness for improved scalability, and full-featured DevOps support.}}, author = {{Dräxler, Sevil and Karl, Holger and Peuster, Manuel and Razzaghi Kouchaksaraei, Hadi and Bredel, Michael and Lessmann, Johannes and Soenen, Thomas and Tavernier, Wouter and Mendel-Brin, Sharon and Xilouris, George}}, booktitle = {{2017 IEEE International Conference on Communications Workshops (ICC Workshops)}}, isbn = {{9781509015252}}, location = {{Paris, France}}, publisher = {{IEEE}}, title = {{{SONATA: Service programming and orchestration for virtualized software networks}}}, doi = {{10.1109/iccw.2017.7962785}}, year = {{2017}}, } @techreport{72, abstract = {{Software verification competitions, such as the annual SV-COMP, evaluate software verification tools with respect to their effectivity and efficiency. Typically, the outcome of a competition is a (possibly category-specific) ranking of the tools. For many applications, such as building portfolio solvers, it would be desirable to have an idea of the (relative) performance of verification tools on a given verification task beforehand, i.e., prior to actually running all tools on the task.In this paper, we present a machine learning approach to predicting rankings of tools on verification tasks. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for verification tasks. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy. In particular, our method outperforms a recently proposed feature-based approach of Demyanova et al. (when applied to rank predictions). }}, author = {{Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, title = {{{Predicting Rankings of Software Verification Competitions}}}, year = {{2017}}, } @inproceedings{723, abstract = {{Developing a virtualized network service does not only involve the implementation and configuration of the network functions it is composed of but also its integration and test with management solutions that will control the service in its production environment. These integration tasks require testbeds that offer the needed network function virtualization infrastructure~(NFVI), like OpenStack, introducing a lot of management and maintenance overheads. Such testbed setups become even more complicated when the multi point-of-presence~(PoP) case, with multiple infrastructure installations, is considered. In this demo, we showcase an emulation platform that executes containerized network services in user-defined multi-PoP topologies. The platform does not only allow network service developers to locally test their services but also to connect real-world management and orchestration solutions to the emulated PoPs. During our interactive demonstration we focus on the integration between the emulated infrastructure and state-of-the-art orchestration solutions like SONATA or OSM.}}, author = {{Peuster, Manuel and Dräxler, Sevil and Razzaghi Kouchaksaraei, Hadi and van Rossem, Steven and Tavernier, Wouter and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization, NetSoft 2017, Bologna, Italy, July 3-7, 2017}}, location = {{Bologna}}, pages = {{1----3}}, title = {{{A flexible multi-pop infrastructure emulator for carrier-grade MANO systems}}}, doi = {{10.1109/NETSOFT.2017.8004250}}, year = {{2017}}, } @inproceedings{73, abstract = {{Today, verification tools do not only output yes or no, but also provide correctness arguments or counterexamples. While counterexamples help to fix bugs, correctness arguments are used to increase the trust in program correctness, e.g., in Proof-Carrying Code (PCC). Correctness arguments are well-studied for single analyses, but not when a set of analyses together verifies a program, each of the analyses checking only a particular part. Such a set of partial, complementary analyses is often used when a single analysis would fail or is inefficient on some program parts.We propose PART_PW, a technique which allows us to automatically construct a proof witness (correctness argument) from the analysis results obtained by a set of partial, complementary analyses. The constructed proof witnesses are proven to be valid correctness arguments and in our experiments we use them seamlessly and efficiently in existing PCC approaches.}}, author = {{Jakobs, Marie-Christine}}, booktitle = {{Software Engineering and Formal Methods}}, editor = {{Cimatti, Alessandro and Sirjani, Marjan}}, pages = {{120--135}}, title = {{{PART_PW: From Partial Analysis Results to a Proof Witness}}}, doi = {{10.1007/978-3-319-66197-1_8}}, year = {{2017}}, } @misc{74, author = {{Knorr, Christoph}}, publisher = {{Universität Paderborn}}, title = {{{OpenCL-basierte Videoverarbeitung auf heterogenen Rechenknoten}}}, year = {{2017}}, }