@inproceedings{22914, author = {{Mohr, Felix and Wever, Marcel Dominik}}, location = {{Virtual}}, title = {{{Replacing the Ex-Def Baseline in AutoML by Naive AutoML}}}, year = {{2021}}, } @inproceedings{22927, author = {{Derrick, John and Doherty, Simon and Dongol, Brijesh and Schellhorn, Gerhard and Wehrheim, Heike}}, booktitle = {{Proceedings of the 35th International Symposium on Distributed Computing (DISC)}}, publisher = {{Schloß Dagstuhl}}, title = {{{On Strong Observational Refinement and Forward Simulation}}}, year = {{2021}}, } @inproceedings{22959, author = {{Weidmann, Nils and Engels, Gregor}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, location = {{Lille, France}}, title = {{{Concurrent model synchronisation with multiple objectives}}}, doi = {{10.1145/3449639.3459283}}, year = {{2021}}, } @inproceedings{23374, author = {{Kummita, Sriteja and Piskachev, Goran and Spath, Johannes and Bodden, Eric}}, booktitle = {{2021 International Conference on Code Quality (ICCQ)}}, title = {{{Qualitative and Quantitative Analysis of Callgraph Algorithms for Python}}}, doi = {{10.1109/iccq51190.2021.9392986}}, year = {{2021}}, } @inproceedings{21727, abstract = {{Platform-based business models underlie the success of many of today’s largest, fastest-growing, and most disruptive companies. Despite the success of prominent examples, such as Uber and Airbnb, creating a profitable platform ecosystem presents a key challenge for many companies across all industries. Although research provides knowledge about platforms’ different value drivers (e.g., network effects), companies that seek to transform their current business model into a platform-based one lack an artifact to reduce knowledge boundaries, collaborate effectively, and cope with the complexities and dynamics of platform ecosystems. We address this challenge by developing two artifacts and combining research from variability modeling, business model dependencies, and system dynamics. This paper presents a design science research approach to develop the platform ecosystem modeling language and the platform ecosystem development tool that support researcher and practitioner by visualizing and simulating platform ecosystems. }}, author = {{Vorbohle, Christian and Gottschalk, Sebastian}}, booktitle = {{Proceedings of the 29th European Conference on Information Systems (ECIS)}}, keywords = {{Platform Ecosystems, Platform Ecosystem Modeling Language, Platform Ecosystem Development Tool, Business Models, Design Science}}, location = {{Virtual Conference/Workshop}}, publisher = {{AIS}}, title = {{{Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems }}}, year = {{2021}}, } @article{21808, abstract = {{Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.}}, author = {{Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}}, journal = {{Transactions on Network and Service Management}}, keywords = {{network management, service management, coordination, reinforcement learning, self-learning, self-adaptation, multi-objective}}, publisher = {{IEEE}}, title = {{{Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}}}, doi = {{10.1109/TNSM.2021.3076503}}, year = {{2021}}, } @inbook{22057, abstract = {{We construct more efficient cryptosystems with provable security against adaptive attacks, based on simple and natural hardness assumptions in the standard model. Concretely, we describe: – An adaptively-secure variant of the efficient, selectively-secure LWE- based identity-based encryption (IBE) scheme of Agrawal, Boneh, and Boyen (EUROCRYPT 2010). In comparison to the previously most efficient such scheme by Yamada (CRYPTO 2017) we achieve smaller lattice parameters and shorter public keys of size O(log λ), where λ is the security parameter. – Adaptively-secure variants of two efficient selectively-secure pairing- based IBEs of Boneh and Boyen (EUROCRYPT 2004). One is based on the DBDH assumption, has the same ciphertext size as the cor- responding BB04 scheme, and achieves full adaptive security with public parameters of size only O(log λ). The other is based on a q- type assumption and has public key size O(λ), but a ciphertext is only a single group element and the security reduction is quadrat- ically tighter than the corresponding scheme by Jager and Kurek (ASIACRYPT 2018). – A very efficient adaptively-secure verifiable random function where proofs, public keys, and secret keys have size O(log λ). As a technical contribution we introduce blockwise partitioning, which leverages the assumption that a cryptographic hash function is weak near-collision resistant to prove full adaptive security of cryptosystems.}}, author = {{Jager, Tibor and Kurek, Rafael and Niehues, David}}, booktitle = {{Public-Key Cryptography – PKC 2021}}, isbn = {{9783030752446}}, issn = {{0302-9743}}, title = {{{Efficient Adaptively-Secure IB-KEMs and VRFs via Near-Collision Resistance}}}, doi = {{10.1007/978-3-030-75245-3_22}}, year = {{2021}}, } @inbook{22059, abstract = {{Verifiable random functions (VRFs), introduced by Micali, Rabin and Vadhan (FOCS’99), are the public-key equivalent of pseudo- random functions. A public verification key and proofs accompanying the output enable all parties to verify the correctness of the output. How- ever, all known standard model VRFs have a reduction loss that is much worse than what one would expect from known optimal constructions of closely related primitives like unique signatures. We show that: 1. Every security proof for a VRF that relies on a non-interactive assumption has to lose a factor of Q, where Q is the number of adver- sarial queries. To that end, we extend the meta-reduction technique of Bader et al. (EUROCRYPT’16) to also cover VRFs. 2. This raises the question: Is this bound optimal? We answer this ques- tion in the affirmative by presenting the first VRF with a reduction from the non-interactive qDBDHI assumption to the security of VRF that achieves this optimal loss. We thus paint a complete picture of the achievability of tight verifiable random functions: We show that a security loss of Q is unavoidable and present the first construction that achieves this bound.}}, author = {{Niehues, David}}, booktitle = {{Public-Key Cryptography – PKC 2021}}, isbn = {{9783030752477}}, issn = {{0302-9743}}, title = {{{Verifiable Random Functions with Optimal Tightness}}}, doi = {{10.1007/978-3-030-75248-4_3}}, year = {{2021}}, } @inproceedings{21953, author = {{Witschen, Linus Matthias and Wiersema, Tobias and Raeisi Nafchi, Masood and Bockhorn, Arne and Platzner, Marco}}, booktitle = {{Proceedings of International Symposium on Applied Reconfigurable Computing (ARC'21)}}, editor = {{Hannig, Frank and Derrien, Steven and Diniz, Pedro and Chillet, Daniel}}, location = {{Virtual conference}}, publisher = {{Springer Lecture Notes in Computer Science}}, title = {{{Timing Optimization for Virtual FPGA Configurations}}}, doi = {{10.1007/978-3-030-79025-7_4}}, year = {{2021}}, } @inproceedings{29235, author = {{Gottschalk, Sebastian and Aziz, Muhammad Suffyan and Yigitbas, Enes and Engels, Gregor}}, booktitle = {{Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings}}, editor = {{Wang, Xiaofeng and Martini, Antonio and Nguyen-Duc, Anh and Stray, Viktoria}}, pages = {{205–220}}, publisher = {{Springer}}, title = {{{Design Principles for a Crowd-Based Prototype Validation Platform}}}, doi = {{10.1007/978-3-030-91983-2_16}}, volume = {{434}}, year = {{2021}}, } @inbook{25528, abstract = {{Developing effective business models is a complex process for a company where several tasks (e.g., conduct customer interviews) need to be accomplished, and decisions (e.g., advertisement as a revenue stream) must be made. Here, domain experts can guide the choices of tasks and decisions with their knowledge. Nevertheless, this knowledge needs to match the situation of the company (e.g., financial resources) and the application domain of the product/service (e.g., mobile app) to reduce the risk of developing ineffective business models with low market penetration. This is not covered by one-size-fits-all development methods without tailoring before the enaction. Therefore, we conduct a design science study to create a situation-specific development approach for business models. Based on situational method engineering and our previous work in storing knowledge of methods and models in distinct repositories, this paper shows the situation-specific composition and enaction of business model development methods. First, the method engineer composes the development method out of both repositories based on the situational context. Second, the business developer enacts the method and develops the business model. We implement the approach in a tool and evaluate it with a industrial case study on mobile apps.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}}, booktitle = {{Product-focused Software Process Improvement}}, keywords = {{Business Model Development, Situational Method Engineering, Lean Development, Kanban Boards, Canvas Models}}, location = {{Turin}}, publisher = {{Springer}}, title = {{{Situation- and Domain-specific Composition and Enactment of Business Model Development Methods}}}, year = {{2021}}, } @inproceedings{30084, author = {{Karakaya, Kadiray and Bodden, Eric}}, booktitle = {{2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM)}}, publisher = {{IEEE}}, title = {{{SootFX: A Static Code Feature Extraction Tool for Java and Android}}}, doi = {{10.1109/scam52516.2021.00030}}, year = {{2021}}, } @inproceedings{25297, author = {{Alshomary, Milad and Gurcke, Timon and Syed, Shahbaz and Heinisch, Philipp and Spliethöver, Maximilian and Cimiano, Philipp and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 8th Workshop on Argument Mining}}, pages = {{184 -- 189}}, title = {{{Key Point Analysis via Contrastive Learning and Extractive Argument Summarization}}}, year = {{2021}}, } @inproceedings{25294, author = {{Nouri, Zahra and Prakash, Nikhil and Gadiraju, Ujwal and Wachsmuth, Henning}}, booktitle = {{Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021}}, title = {{{iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing}}}, year = {{2021}}, } @inproceedings{30217, author = {{Coy, Sam and Czumaj, Artur and Feldmann, Michael and Hinnenthal, Kristian and Kuhn, Fabian and Scheideler, Christian and Schneider, Philipp and Struijs, Martijn}}, booktitle = {{25th International Conference on Principles of Distributed Systems, OPODIS 2021, December 13-15, 2021, Strasbourg, France}}, editor = {{Bramas, Quentin and Gramoli, Vincent and Milani, Alessia}}, pages = {{11:1–11:23}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{Near-Shortest Path Routing in Hybrid Communication Networks}}}, doi = {{10.4230/LIPIcs.OPODIS.2021.11}}, volume = {{217}}, year = {{2021}}, } @inproceedings{21593, author = {{Yigitbas, Enes and Jovanovikj, Ivan and Engels, Gregor}}, booktitle = {{Proceedings of the 18th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2021) }}, publisher = {{Springer}}, title = {{{Simplifying Robot Programming using Augmented Reality and End-User Development}}}, year = {{2021}}, } @inproceedings{21707, author = {{Yigitbas, Enes and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Proceedings of the 13th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2021)}}, publisher = {{ACM}}, title = {{{Using Augmented Reality for Enhancing Planning and Measurements in the Scaffolding Business}}}, year = {{2021}}, } @inproceedings{22706, author = {{Yigitbas, Enes and Gorissen, Simon and Weidmann, Nils and Engels, Gregor}}, booktitle = {{Proceedings of the 24th International Conference on Model Driven Engineering Languages and Systems (MODELS'21) }}, publisher = {{ACM/IEEE}}, title = {{{Collaborative Software Modeling in Virtual Reality}}}, year = {{2021}}, } @inproceedings{21598, abstract = {{Static analysis is used to automatically detect bugs and security breaches, and aids compileroptimization. Whole-program analysis (WPA) can yield high precision, however causes long analysistimes and thus does not match common software-development workflows, making it often impracticalto use for large, real-world applications.This paper thus presents the design and implementation ofModAlyzer, a novel static-analysisapproach that aims at accelerating whole-program analysis by making the analysis modular andcompositional. It shows how to computelossless, persisted summaries for callgraph, points-to anddata-flow information, and it reports under which circumstances this function-level compositionalanalysis outperforms WPA.We implementedModAlyzeras an extension to LLVM and PhASAR, and applied it to 12 real-world C and C++ applications. At analysis time,ModAlyzermodularly and losslessly summarizesthe analysis effect of the library code those applications share, hence avoiding its repeated re-analysis.The experimental results show that the reuse of these summaries can save, on average, 72% ofanalysis time over WPA. Moreover, because it is lossless, the module-wise analysis fully retainsprecision and recall. Surprisingly, as our results show, it sometimes even yields precision superior toWPA. The initial summary generation, on average, takes about 3.67 times as long as WPA.}}, author = {{Schubert, Philipp and Hermann, Ben and Bodden, Eric}}, booktitle = {{European Conference on Object-Oriented Programming (ECOOP)}}, title = {{{Lossless, Persisted Summarization of Static Callgraph, Points-To and Data-Flow Analysis}}}, year = {{2021}}, } @inproceedings{27381, abstract = {{Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression.}}, author = {{Damke, Clemens and Hüllermeier, Eyke}}, booktitle = {{Proceedings of The 24th International Conference on Discovery Science (DS 2021)}}, editor = {{Soares, Carlos and Torgo, Luis}}, isbn = {{9783030889418}}, issn = {{0302-9743}}, keywords = {{Graph-structured data, Graph neural networks, Preference learning, Learning to rank}}, location = {{Halifax, Canada}}, pages = {{166--180}}, publisher = {{Springer}}, title = {{{Ranking Structured Objects with Graph Neural Networks}}}, doi = {{10.1007/978-3-030-88942-5}}, volume = {{12986}}, year = {{2021}}, }