@misc{20820, author = {{Thiele, Simon}}, title = {{{Implementing Machine Learning Functions as PYNQ FPGA Overlays}}}, year = {{2020}}, } @misc{20821, author = {{Jaganath, Vivek}}, title = {{{Extension and Evaluation of Python-based High-Level Synthesis Tool Flows}}}, year = {{2020}}, } @misc{18066, author = {{Skowronek, Michael}}, publisher = {{Universität Paderborn}}, title = {{{Approaches for Competetive Routing through Intersections of Hole Abstractions in Hybrid Communication Networks}}}, year = {{2020}}, } @inproceedings{18249, abstract = {{Augmented Reality (AR) has recently found high attention in mobile shopping apps such as in domains like furniture or decoration. Here, the developers of the apps focus on the positioning of atomic 3D objects in the physical environment. With this focus, they neglect the configuration of multi-faceted 3D object composition according to the user needs and environmental constraints. To tackle these challenges, we present a model-based approach to support AR-assisted product con-figuration based on the concept of Dynamic Software Product Lines. Our approach splits products (e.g. table) into parts (e.g. tabletop, ta-ble legs, funnier) with their 3D objects and additional information (e.g. name, price). The possible products, which can be configured out of these parts, are stored in a feature model. At runtime, this feature model can be used to configure 3D object compositions out of the product parts and adapt to user needs and environmental constraints. The benefits of this approach are demonstrated by a case study of configuring modular kitchens with the help of a prototypical mobile-based implementation.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering. HCSE 2020}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, keywords = {{Product Configuration, Augmented Reality, Runtime Adaptation, Dynamic Software Product Lines}}, location = {{Eindhoven}}, publisher = {{Springer}}, title = {{{Model-based Product Configuration in Augmented Reality Applications}}}, doi = {{10.1007/978-3-030-64266-2_5}}, volume = {{12481}}, year = {{2020}}, } @inproceedings{18276, abstract = {{Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Due to possibly extremely long runtimes of candidate algorithms, training data for algorithm selection models is usually generated under time constraints in the sense that not all algorithms are run to completion on all instances. Thus, training data usually comprises censored information, as the true runtime of algorithms timed out remains unknown. However, many standard AS approaches are not able to handle such information in a proper way. On the other side, survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime, as we demonstrate in this work. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of a framework of this kind, we advocate a risk-averse approach to algorithm selection, in which the avoidance of a timeout is given high priority. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.}}, author = {{Tornede, Alexander and Wever, Marcel Dominik and Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{ACML 2020}}, location = {{Bangkok, Thailand}}, title = {{{Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis}}}, year = {{2020}}, } @phdthesis{18520, author = {{Setzer, Alexander}}, publisher = {{Universität Paderborn}}, title = {{{Local Graph Transformation Primitives For Some Basic Problems In Overlay Networks}}}, doi = {{10.17619/UNIPB/1-1026}}, year = {{2020}}, } @misc{18637, author = {{Schürmann, Patrick}}, publisher = {{Universität Paderborn}}, title = {{{A Group Signature Scheme from Flexible Public Key Signatures and Structure-Preserving Signatures on Equivalence Classes}}}, year = {{2020}}, } @misc{18639, author = {{Terfort, Tobias}}, publisher = {{Universität Paderborn}}, title = {{{Enhancing Security by Usage of Universal One-Way Hash Functions}}}, year = {{2020}}, } @misc{18648, author = {{Guggenmos, Andreas}}, publisher = {{Universität Paderborn}}, title = {{{Algorithmen für selbststabilisierende Skip+-Delaunaygraphen}}}, year = {{2020}}, } @techreport{22161, author = {{Kundisch, Dennis}}, pages = {{1}}, title = {{{Digitale mehrseitige Plattformen – Besser verstehen, wie digitale Plattformen funktionieren}}}, volume = {{3}}, year = {{2020}}, } @article{6202, author = {{Szopinski, Daniel and Schoormann, T. and John, Thomas and Knackstedt, R. and Kundisch, Dennis}}, journal = {{Electronic Markets}}, number = {{3}}, pages = {{469--494}}, title = {{{Software tools for business model innovation: Current state and future challenges}}}, volume = {{30}}, year = {{2020}}, } @article{4627, author = {{Kaimann, Daniel}}, journal = {{Applied Economics Letters}}, number = {{16}}, pages = {{1366--1370}}, title = {{{Ancillary market signaling: A two-stage model of economic reputation on ancillary market success}}}, doi = {{10.1080/13504851.2019.1683136}}, volume = {{27}}, year = {{2020}}, } @inproceedings{16487, author = {{Bobolz, Jan and Eidens, Fabian and Krenn, Stephan and Slamanig, Daniel and Striecks, Christoph}}, booktitle = {{Proceedings of the 15th ACM Asia Conference on Computer and Communications Security (ASIA CCS ’20),}}, location = {{Taiwan}}, publisher = {{ACM}}, title = {{{Privacy-Preserving Incentive Systems with Highly Efficient Point-Collection}}}, doi = {{10.1145/3320269.3384769}}, year = {{2020}}, } @inproceedings{16724, author = {{Sharma, Arnab and Wehrheim, Heike}}, booktitle = {{Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA).}}, publisher = {{ACM}}, title = {{{Higher Income, Larger Loan? Monotonicity Testing of Machine Learning Models}}}, year = {{2020}}, } @article{16725, author = {{Richter, Cedric and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}}, journal = {{Journal of Automated Software Engineering}}, publisher = {{Springer}}, title = {{{Algorithm Selection for Software Validation Based on Graph Kernels}}}, year = {{2020}}, } @inproceedings{16726, author = {{Razzaghi Kouchaksaraei, Hadi and Shivarpatna Venkatesh, Ashwin Prasad and Churi, Amey and Illian, Marvin and Karl, Holger}}, booktitle = {{European Conference on Networks and Communications (EUCNC 2020)}}, title = {{{Dynamic Provisioning of Network Services on Heterogeneous Resources}}}, year = {{2020}}, } @article{16902, abstract = {{The maintenance of efficient and robust overlay networks is one of the most fundamental and reoccurring themes in networking. This paper presents a survey of state-of-the-art algorithms to design and repair overlay networks in a distributed manner. In particular, we discuss basic algorithmic primitives to preserve connectivity, review algorithms for the fundamental problem of graph linearization, and then survey self-stabilizing algorithms for metric and scalable topologies. We also identify open problems and avenues for future research. }}, author = {{Feldmann, Michael and Scheideler, Christian and Schmid, Stefan}}, journal = {{ACM Computing Surveys}}, publisher = {{ACM}}, title = {{{Survey on Algorithms for Self-Stabilizing Overlay Networks}}}, doi = {{10.1145/3397190}}, year = {{2020}}, } @phdthesis{16910, author = {{Stroh-Maraun, Nadja}}, publisher = {{Universität Paderborn}}, title = {{{Mechanisms, Preferences, and Heterogeneity in Matching Markets}}}, doi = {{10.17619/UNIPB/1-958}}, year = {{2020}}, } @inproceedings{16933, abstract = {{The continuous innovation of its business models is an important task for a company to stay competitive. During this process, the company has to validate various hypotheses about its business models by adapting to uncertain and changing customer needs effectively and efficiently. This adaptation, in turn, can be supported by the concept of Software Product Lines (SPLs). SPLs reduce the time to market by deriving products for customers with changing requirements using a common set of features, structured as a feature model. Analogously, we support the process of business model adaptation by applying the engineering process of SPLs to the structure of the Business Model Canvas (BMC). We call this concept a Business Model Decision Line (BMDL). The BMDL matches business domain knowledge in the form of a feature model with customer needs to derive hypotheses about the business model together with experiments for validation. Our approach is effective by providing a comprehensive overview of possible business model adaptations and efficient by reusing experiments for different hypotheses. We implement our approach in a tool and illustrate the usefulness with an example of developing business models for a mobile application.}}, author = {{Gottschalk, Sebastian and Rittmeier, Florian and Engels, Gregor}}, booktitle = {{Proceedings of the 22nd IEEE International Conference on Business Informatics}}, keywords = {{Business Model Decision Line, Business Model Adaptation, Hypothesis-driven Adaptation, Software Product Line, Feature Model}}, location = {{Antwerp}}, publisher = {{IEEE}}, title = {{{Hypothesis-driven Adaptation of Business Models based on Product Line Engineering}}}, doi = {{10.1109/CBI49978.2020.00022}}, year = {{2020}}, } @inproceedings{16934, abstract = {{To build successful products, the developers have to adapt their product features and business models to uncertain customer needs. This adaptation is part of the research discipline of Hypotheses Engineering (HE) where customer needs can be seen as hypotheses that need to be tested iteratively by conducting experiments together with the customer. So far, modeling support and associated traceability of this iterative process are missing. Both, in turn, are important to document the adaptation to the customer needs and identify experiments that provide most evidence to the customer needs. To target this issue, we introduce a model-based HE approach with a twofold contribution: First, we develop a modeling language that models hypotheses and experiments as interrelated hierarchies together with a mapping between them. While the hypotheses are labeled with a score level of their current evidence, the experiments are labeled with a score level of maximum evidence that can be achieved during conduction. Second, we provide an iterative process to determine experiments that offer the most evidence improvement to the modeled hypotheses. We illustrate the usefulness of the approach with an example of testing the business model of a mobile application.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Engels, Gregor}}, booktitle = {{Business Modeling and Software Design}}, editor = {{Shishkov, Boris}}, keywords = {{Hypothesis Engineering, Model-based, Customer Need Adaptation, Business Model, Product Features}}, location = {{Potsdam}}, pages = {{276--286}}, publisher = {{Springer International Publishing}}, title = {{{Model-based Hypothesis Engineering for Supporting Adaptation to Uncertain Customer Needs}}}, doi = {{10.1007/978-3-030-52306-0_18}}, volume = {{391}}, year = {{2020}}, }