@inproceedings{43395, author = {{Trentinaglia, Roman and Merschjohann, Sven and Fockel, Markus and Eikerling, Hendrik}}, booktitle = {{REFSQ 2023: Requirements Engineering: Foundation for Software Quality}}, isbn = {{9783031297854}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Eliciting Security Requirements – An Experience Report}}}, doi = {{10.1007/978-3-031-29786-1_25}}, year = {{2023}}, } @inbook{44769, author = {{Castenow, Jannik and Harbig, Jonas and Meyer auf der Heide, Friedhelm}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031304477}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Unifying Gathering Protocols for Swarms of Mobile Robots}}}, doi = {{10.1007/978-3-031-30448-4_1}}, year = {{2023}}, } @inbook{46516, abstract = {{Linked knowledge graphs build the backbone of many data-driven applications such as search engines, conversational agents and e-commerce solutions. Declarative link discovery frameworks use complex link specifications to express the conditions under which a link between two resources can be deemed to exist. However, understanding such complex link specifications is a challenging task for non-expert users of link discovery frameworks. In this paper, we address this drawback by devising NMV-LS, a language model-based verbalization approach for translating complex link specifications into natural language. NMV-LS relies on the results of rule-based link specification verbalization to apply continuous training on T5, a large language model based on the Transformerarchitecture. We evaluated NMV-LS on English and German datasets using well-known machine translation metrics such as BLUE, METEOR, ChrF++ and TER. Our results suggest that our approach achieves a verbalization performance close to that of humans and outperforms state of the art approaches. Our source code and datasets are publicly available at https://github.com/dice-group/NMV-LS.}}, author = {{Ahmed, Abdullah Fathi Ahmed and Firmansyah, Asep Fajar and Sherif, Mohamed and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Natural Language Processing and Information Systems}}, isbn = {{9783031353192}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Explainable Integration of Knowledge Graphs Using Large Language Models}}}, doi = {{10.1007/978-3-031-35320-8_9}}, year = {{2023}}, } @inbook{46572, abstract = {{Indonesian is classified as underrepresented in the Natural Language Processing (NLP) field, despite being the tenth most spoken language in the world with 198 million speakers. The paucity of datasets is recognized as the main reason for the slow advancements in NLP research for underrepresented languages. Significant attempts were made in 2020 to address this drawback for Indonesian. The Indonesian Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT pre-trained language model. The second benchmark, Indonesian Language Evaluation Montage (IndoLEM), was presented in the same year. These benchmarks support several tasks, including Named Entity Recognition (NER). However, all NER datasets are in the public domain and do not contain domain-specific datasets. To alleviate this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset in the religious domain that adheres to a meticulously designed annotation guideline. Since Indonesia has the world’s largest Muslim population, we build the dataset from the Indonesian translation of the Quran. The dataset includes 2475 named entities representing 18 different classes. To assess the annotation quality of IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT fine-tuning. The results reveal that the first model outperforms the second model achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable evaluation metric for Indonesian NER tasks in the aforementioned domain, widening the research’s domain range.}}, author = {{Gusmita, Ria Hari and Firmansyah, Asep Fajar and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Natural Language Processing and Information Systems}}, isbn = {{9783031353192}}, issn = {{0302-9743}}, location = {{Derby, UK}}, publisher = {{Springer Nature Switzerland}}, title = {{{IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran}}}, doi = {{10.1007/978-3-031-35320-8_12}}, year = {{2023}}, } @inbook{46867, author = {{Dieter, Peter}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031436116}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{A Regret Policy for the Dynamic Vehicle Routing Problem with Time Windows}}}, doi = {{10.1007/978-3-031-43612-3_14}}, year = {{2023}}, } @inbook{47421, abstract = {{Class expression learning in description logics has long been regarded as an iterative search problem in an infinite conceptual space. Each iteration of the search process invokes a reasoner and a heuristic function. The reasoner finds the instances of the current expression, and the heuristic function computes the information gain and decides on the next step to be taken. As the size of the background knowledge base grows, search-based approaches for class expression learning become prohibitively slow. Current neural class expression synthesis (NCES) approaches investigate the use of neural networks for class expression learning in the attributive language with complement (ALC). While they show significant improvements over search-based approaches in runtime and quality of the computed solutions, they rely on the availability of pretrained embeddings for the input knowledge base. Moreover, they are not applicable to ontologies in more expressive description logics. In this paper, we propose a novel NCES approach which extends the state of the art to the description logic ALCHIQ(D). Our extension, dubbed NCES2, comes with an improved training data generator and does not require pretrained embeddings for the input knowledge base as both the embedding model and the class expression synthesizer are trained jointly. Empirical results on benchmark datasets suggest that our approach inherits the scalability capability of current NCES instances with the additional advantage that it supports more complex learning problems. NCES2 achieves the highest performance overall when compared to search-based approaches and to its predecessor NCES. We provide our source code, datasets, and pretrained models at https://github.com/dice-group/NCES2.}}, author = {{Kouagou, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Machine Learning and Knowledge Discovery in Databases: Research Track}}, isbn = {{9783031434204}}, issn = {{0302-9743}}, location = {{Turin}}, publisher = {{Springer Nature Switzerland}}, title = {{{Neural Class Expression Synthesis in ALCHIQ(D)}}}, doi = {{10.1007/978-3-031-43421-1_12}}, year = {{2023}}, } @article{47953, author = {{Kornowicz, Jaroslaw and Thommes, Kirsten}}, isbn = {{9783031358906}}, issn = {{0302-9743}}, journal = {{Artificial Intelligence in HCI}}, publisher = {{Springer Nature Switzerland}}, title = {{{Aggregating Human Domain Knowledge for Feature Ranking}}}, doi = {{10.1007/978-3-031-35891-3_7}}, year = {{2023}}, } @inproceedings{50479, abstract = {{Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. However, most fact-checking approaches are binary classifiers that fail to consider the volatility of some assertions, i.e., the fact that such assertions are only valid at certain times or for specific time intervals. Moreover, the few approaches able to predict when an assertion was valid (i.e., time-point prediction approaches) rely on manual feature engineering. This paper presents TEMPORALFC, a temporal fact-checking approach that uses multiple sources of background knowledge to assess the veracity and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets and compare it to the state of the art in fact-checking and time-point prediction. Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC.}}, author = {{Qudus, Umair and Röder, Michael and Kirrane, Sabrina and Ngomo, Axel-Cyrille Ngonga}}, booktitle = {{The Semantic Web – ISWC 2023}}, editor = {{R. Payne, Terry and Presutti, Valentina and Qi, Guilin and Poveda-Villalón, María and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong and Li, Juanzi}}, isbn = {{9783031472398}}, issn = {{0302-9743}}, keywords = {{temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs}}, location = {{Athens, Greece}}, pages = {{465–483}}, publisher = {{Springer, Cham}}, title = {{{TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs}}}, doi = {{10.1007/978-3-031-47240-4_25}}, volume = {{14265}}, year = {{2023}}, } @inbook{46191, author = {{Alt, Christoph and Kenter, Tobias and Faghih-Naini, Sara and Faj, Jennifer and Opdenhövel, Jan-Oliver and Plessl, Christian and Aizinger, Vadym and Hönig, Jan and Köstler, Harald}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031320408}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Shallow Water DG Simulations on FPGAs: Design and Comparison of a Novel Code Generation Pipeline}}}, doi = {{10.1007/978-3-031-32041-5_5}}, year = {{2023}}, } @inproceedings{51373, author = {{Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}}, booktitle = {{26th International Conference on Discovery Science }}, isbn = {{9783031452741}}, issn = {{0302-9743}}, location = {{Porto}}, pages = {{189--203}}, publisher = {{Springer Nature Switzerland}}, title = {{{Probabilistic Scoring Lists for Interpretable Machine Learning}}}, doi = {{10.1007/978-3-031-45275-8_13}}, volume = {{14050}}, year = {{2023}}, } @inbook{48776, author = {{Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}}, booktitle = {{Machine Learning and Knowledge Discovery in Databases: Research Track}}, isbn = {{9783031434174}}, issn = {{1611-3349}}, publisher = {{Springer Nature Switzerland}}, title = {{{iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams}}}, doi = {{10.1007/978-3-031-43418-1_26}}, year = {{2023}}, } @inbook{52859, author = {{de Camargo e Souza Câmara, Igor and Turhan, Anni-Yasmin}}, booktitle = {{Logics in Artificial Intelligence}}, isbn = {{9783031436185}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Deciding Subsumption in Defeasible $$\mathcal {ELI}_\bot $$ with Typicality Models}}}, doi = {{10.1007/978-3-031-43619-2_36}}, year = {{2023}}, } @inproceedings{30971, author = {{Hansmeier, Tim and Platzner, Marco}}, booktitle = {{Applications of Evolutionary Computation, EvoApplications 2022, Proceedings}}, isbn = {{9783031024610}}, issn = {{0302-9743}}, location = {{Madrid}}, pages = {{386--401}}, publisher = {{Springer International Publishing}}, title = {{{Integrating Safety Guarantees into the Learning Classifier System XCS}}}, doi = {{10.1007/978-3-031-02462-7_25}}, volume = {{13224}}, year = {{2022}}, } @inbook{34077, author = {{Bondarenko, Alexander and Fröbe, Maik and Kiesel, Johannes and Syed, Shahbaz and Gurcke, Timon and Beloucif, Meriem and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and Potthast, Martin and Hagen, Matthias}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030997380}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Overview of Touché 2022: Argument Retrieval}}}, doi = {{10.1007/978-3-030-99739-7_43}}, year = {{2022}}, } @inbook{34292, author = {{Wolters, Dennis and Engels, Gregor}}, booktitle = {{Product-Focused Software Process Improvement}}, editor = {{Taibi, Davide and Kuhrmann, Marco and Mikkonen, Tommi and Klünder, Jil and Abrahamsson, Pekka}}, isbn = {{9783031213878}}, issn = {{0302-9743}}, pages = {{235--242}}, publisher = {{Springer International Publishing}}, title = {{{Towards Situational Process Management for Professional Education Programmes}}}, doi = {{10.1007/978-3-031-21388-5_16}}, volume = {{13709}}, year = {{2022}}, } @inbook{29872, author = {{Maack, Marten and Meyer auf der Heide, Friedhelm and Pukrop, Simon}}, booktitle = {{Approximation and Online Algorithms}}, isbn = {{9783030927011}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Server Cloud Scheduling}}}, doi = {{10.1007/978-3-030-92702-8_10}}, year = {{2022}}, } @inbook{33740, author = {{KOUAGOU, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web}}, isbn = {{9783031069802}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Learning Concept Lengths Accelerates Concept Learning in ALC}}}, doi = {{10.1007/978-3-031-06981-9_14}}, year = {{2022}}, } @inbook{29727, author = {{Wohlleben, Meike Claudia and Bender, Amelie and Peitz, Sebastian and Sextro, Walter}}, booktitle = {{Machine Learning, Optimization, and Data Science}}, isbn = {{9783030954697}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction}}}, doi = {{10.1007/978-3-030-95470-3_8}}, year = {{2022}}, } @inbook{33738, author = {{Zahera, Hamada Mohamed Abdelsamee and Heindorf, Stefan and Balke, Stefan and Haupt, Jonas and Voigt, Martin and Walter, Carolin and Witter, Fabian and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web: ESWC 2022 Satellite Events}}, isbn = {{9783031116087}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings}}}, doi = {{10.1007/978-3-031-11609-4_9}}, year = {{2022}}, } @inbook{38506, author = {{Zahera, H.M.A and Vollmers, Daniel and Sherif, Mohamed Ahmed and Ngomo, Axel-Cyrille Ngonga}}, booktitle = {{The Semantic Web – ISWC 2022}}, isbn = {{9783031194320}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{MultPAX: Keyphrase Extraction Using Language Models and Knowledge Graphs}}}, doi = {{10.1007/978-3-031-19433-7_18}}, year = {{2022}}, } @inbook{29046, author = {{Feldhans, Robert and Wilke, Adrian and Heindorf, Stefan and Shaker, Mohammad Hossein and Hammer, Barbara and Ngonga Ngomo, Axel-Cyrille and Hüllermeier, Eyke}}, booktitle = {{Intelligent Data Engineering and Automated Learning – IDEAL 2021}}, isbn = {{9783030916077}}, issn = {{0302-9743}}, title = {{{Drift Detection in Text Data with Document Embeddings}}}, doi = {{10.1007/978-3-030-91608-4_11}}, year = {{2021}}, } @inproceedings{21378, author = {{Hartel, Rita and Dunst, Alexander}}, booktitle = {{MANPU 2020: The 4th International Workshop on coMics ANalysis, Processing and Understanding@Pattern Recognition. ICPR International Workshops and Challenges}}, isbn = {{9783030687793}}, issn = {{0302-9743}}, title = {{{An OCR Pipeline and Semantic Text Analysis for Comics}}}, doi = {{10.1007/978-3-030-68780-9_19}}, 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{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}}, } @inbook{26888, author = {{Götte, Thorsten and Kolb, Christina and Scheideler, Christian and Werthmann, Julian}}, booktitle = {{Algorithms for Sensor Systems (ALGOSENSORS '21)}}, issn = {{0302-9743}}, location = {{Lisbon, Portgual}}, title = {{{Beep-And-Sleep: Message and Energy Efficient Set Cover}}}, doi = {{10.1007/978-3-030-89240-1_7}}, year = {{2021}}, } @inproceedings{29566, author = {{Bobolz, Jan and Eidens, Fabian and Krenn, Stephan and Ramacher, Sebastian and Samelin, Kai}}, booktitle = {{Cryptology and Network Security}}, isbn = {{9783030925475}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Issuer-Hiding Attribute-Based Credentials}}}, doi = {{10.1007/978-3-030-92548-2_9}}, year = {{2021}}, } @inbook{32868, author = {{Nagbøl, Per Rådberg and Müller, Oliver and Krancher, Oliver}}, booktitle = {{The Next Wave of Sociotechnical Design}}, isbn = {{9783030824044}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Designing a Risk Assessment Tool for Artificial Intelligence Systems}}}, doi = {{10.1007/978-3-030-82405-1_32}}, year = {{2021}}, } @inbook{29292, author = {{Feldhans, Robert and Wilke, Adrian and Heindorf, Stefan and Shaker, Mohammad Hossein and Hammer, Barbara and Ngonga Ngomo, Axel-Cyrille and Hüllermeier, Eyke}}, booktitle = {{Intelligent Data Engineering and Automated Learning – IDEAL 2021}}, isbn = {{9783030916077}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Drift Detection in Text Data with Document Embeddings}}}, doi = {{10.1007/978-3-030-91608-4_11}}, year = {{2021}}, } @inproceedings{45846, author = {{Kontinen, Juha and Meier, Arne and Mahmood, Yasir}}, booktitle = {{Logical Foundations of Computer Science}}, isbn = {{9783030930998}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{A Parameterized View on the Complexity of Dependence Logic}}}, doi = {{10.1007/978-3-030-93100-1_9}}, year = {{2021}}, } @inbook{21587, abstract = {{Solving partial differential equations on unstructured grids is a cornerstone of engineering and scientific computing. Nowadays, heterogeneous parallel platforms with CPUs, GPUs, and FPGAs enable energy-efficient and computationally demanding simulations. We developed the HighPerMeshes C++-embedded Domain-Specific Language (DSL) for bridging the abstraction gap between the mathematical and algorithmic formulation of mesh-based algorithms for PDE problems on the one hand and an increasing number of heterogeneous platforms with their different parallel programming and runtime models on the other hand. Thus, the HighPerMeshes DSL aims at higher productivity in the code development process for multiple target platforms. We introduce the concepts as well as the basic structure of the HighPerMeshes DSL, and demonstrate its usage with three examples, a Poisson and monodomain problem, respectively, solved by the continuous finite element method, and the discontinuous Galerkin method for Maxwell’s equation. The mapping of the abstract algorithmic description onto parallel hardware, including distributed memory compute clusters, is presented. Finally, the achievable performance and scalability are demonstrated for a typical example problem on a multi-core CPU cluster.}}, author = {{Alhaddad, Samer and Förstner, Jens and Groth, Stefan and Grünewald, Daniel and Grynko, Yevgen and Hannig, Frank and Kenter, Tobias and Pfreundt, Franz-Josef and Plessl, Christian and Schotte, Merlind and Steinke, Thomas and Teich, Jürgen and Weiser, Martin and Wende, Florian}}, booktitle = {{Euro-Par 2020: Parallel Processing Workshops}}, isbn = {{9783030715922}}, issn = {{0302-9743}}, keywords = {{tet_topic_hpc}}, title = {{{HighPerMeshes – A Domain-Specific Language for Numerical Algorithms on Unstructured Grids}}}, doi = {{10.1007/978-3-030-71593-9_15}}, year = {{2021}}, } @inbook{29936, author = {{Ramaswami, Arjun and Kenter, Tobias and Kühne, Thomas and Plessl, Christian}}, booktitle = {{Applied Reconfigurable Computing. Architectures, Tools, and Applications}}, isbn = {{9783030790240}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Evaluating the Design Space for Offloading 3D FFT Calculations to an FPGA for High-Performance Computing}}}, doi = {{10.1007/978-3-030-79025-7_21}}, year = {{2021}}, } @inbook{45823, author = {{Kontinen, Juha and Meier, Arne and Mahmood, Yasir}}, booktitle = {{Logical Foundations of Computer Science}}, isbn = {{9783030930998}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{A Parameterized View on the Complexity of Dependence Logic}}}, doi = {{10.1007/978-3-030-93100-1_9}}, year = {{2021}}, } @inbook{19521, author = {{Pfannschmidt, Karlson and Hüllermeier, Eyke}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030582845}}, issn = {{0302-9743}}, title = {{{Learning Choice Functions via Pareto-Embeddings}}}, doi = {{10.1007/978-3-030-58285-2_30}}, year = {{2020}}, } @inbook{19561, author = {{Sellmann, Meinolf and Tierney, Kevin}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030535513}}, issn = {{0302-9743}}, keywords = {{pc2-ressources}}, title = {{{Hyper-parameterized Dialectic Search for Non-linear Box-Constrained Optimization with Heterogenous Variable Types}}}, doi = {{10.1007/978-3-030-53552-0_12}}, year = {{2020}}, } @inbook{21396, abstract = {{Verifiable random functions (VRFs) are essentially digital signatures with additional properties, namely verifiable uniqueness and pseudorandomness, which make VRFs a useful tool, e.g., to prevent enumeration in DNSSEC Authenticated Denial of Existence and the CONIKS key management system, or in the random committee selection of the Algorand blockchain. Most standard-model VRFs rely on admissible hash functions (AHFs) to achieve security against adaptive attacks in the standard model. Known AHF constructions are based on error-correcting codes, which yield asymptotically efficient constructions. However, previous works do not clarify how the code should be instantiated concretely in the real world. The rate and the minimal distance of the selected code have significant impact on the efficiency of the resulting cryptosystem, therefore it is unclear if and how the aforementioned constructions can be used in practice. First, we explain inherent limitations of code-based AHFs. Concretely, we assume that even if we were given codes that achieve the well-known Gilbert-Varshamov or McEliece-Rodemich-Rumsey-Welch bounds, existing AHF-based constructions of verifiable random functions (VRFs) can only be instantiated quite inefficiently. Then we introduce and construct computational AHFs (cAHFs). While classical AHFs are information-theoretic, and therefore work even in presence of computationally unbounded adversaries, cAHFs provide only security against computationally bounded adversaries. However, we show that cAHFs can be instantiated significantly more efficiently. Finally, we use our cAHF to construct the currently most efficient verifiable random function with full adaptive security in the standard model.}}, author = {{Jager, Tibor and Niehues, David}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030384708}}, issn = {{0302-9743}}, keywords = {{Admissible hash functions, Verifiable random functions, Error-correcting codes, Provable security}}, location = {{Waterloo, Canada}}, title = {{{On the Real-World Instantiability of Admissible Hash Functions and Efficient Verifiable Random Functions}}}, doi = {{10.1007/978-3-030-38471-5_13}}, year = {{2020}}, } @inbook{17337, author = {{Jazayeri, Bahar and Schwichtenberg, Simon and Küster, Jochen and Zimmermann, Olaf and Engels, Gregor}}, booktitle = {{Advanced Information Systems Engineering}}, isbn = {{9783030494346}}, issn = {{0302-9743}}, title = {{{Modeling and Analyzing Architectural Diversity of Open Platforms}}}, doi = {{10.1007/978-3-030-49435-3_3}}, year = {{2020}}, } @inbook{20891, abstract = {{Today, software systems are rarely developed monolithically, but may be composed of numerous individually developed features. Their modularization facilitates independent development and verification. While feature-based strategies to verify features in isolation have existed for years, they cannot address interactions between features. The problem with feature interactions is that they are typically unknown and may involve any subset of the features. Contrary, a family-based verification strategy captures feature interactions, but does not scale well when features evolve frequently. To the best of our knowledge, there currently exists no approach with focus on evolving features that combines both strategies and aims at eliminating their respective drawbacks. To fill this gap, we introduce Fefalution, a feature-family-based verification approach based on abstract contracts to verify evolving features and their interactions. Fefalution builds partial proofs for each evolving feature and then reuses the resulting partial proofs in verifying feature interactions, yielding a full verification of the complete software system. Moreover, to investigate whether a combination of both strategies is fruitful, we present the first empirical study for the verification of evolving features implemented by means of feature-oriented programming and by comparing Fefalution with another five family-based approaches varying in a set of optimizations. Our results indicate that partial proofs based on abstract contracts exhibit huge reuse potential, but also come with a substantial overhead for smaller evolution scenarios. }}, author = {{Knüppel, Alexander and Krüger, Stefan and Thüm, Thomas and Bubel, Richard and Krieter, Sebastian and Bodden, Eric and Schaefer, Ina}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030643539}}, issn = {{0302-9743}}, title = {{{Using Abstract Contracts for Verifying Evolving Features and Their Interactions}}}, doi = {{10.1007/978-3-030-64354-6_5}}, year = {{2020}}, } @inbook{18014, author = {{El Mesaoudi-Paul, Adil and Weiß, Dimitri and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}}, booktitle = {{Learning and Intelligent Optimization. LION 2020.}}, isbn = {{9783030535513}}, issn = {{0302-9743}}, pages = {{216 -- 232}}, publisher = {{Springer}}, title = {{{Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach}}}, doi = {{10.1007/978-3-030-53552-0_22}}, volume = {{12096}}, year = {{2020}}, } @inbook{23377, author = {{Piskachev, Goran and Petrasch, Tobias and Späth, Johannes and Bodden, Eric}}, booktitle = {{Lecture Notes in Computer Science}}, issn = {{0302-9743}}, title = {{{AuthCheck: Program-State Analysis for Access-Control Vulnerabilities}}}, doi = {{10.1007/978-3-030-54997-8_34}}, year = {{2020}}, } @inproceedings{17084, author = {{Weidmann, Nils and Anjorin, Anthony}}, booktitle = {{Proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020}}, editor = {{Wehrheim, Heike and Cabot, Jordi}}, isbn = {{9783030452339}}, issn = {{0302-9743}}, location = {{Dublin, Ireland}}, publisher = {{Springer}}, title = {{{Schema Compliant Consistency Management via Triple Graph Grammars and Integer Linear Programming}}}, doi = {{10.1007/978-3-030-45234-6_16}}, year = {{2020}}, } @inproceedings{17085, author = {{Schwichtenberg, Bahar and Schwichtenberg, Simon and Küster, Jochen and Zimmermann, Olaf and Engels, Gregor}}, booktitle = {{Advanced Information Systems Engineering}}, isbn = {{9783030494346}}, issn = {{0302-9743}}, title = {{{Modeling and Analyzing Architectural Diversity of Open Platforms}}}, doi = {{10.1007/978-3-030-49435-3_3}}, year = {{2020}}, } @inproceedings{28997, abstract = {{Modern cryptographic protocols, such as TLS 1.3 and QUIC, can send cryptographically protected data in “zero round-trip times (0-RTT)”, that is, without the need for a prior interactive handshake. Such protocols meet the demand for communication with minimal latency, but those currently deployed in practice achieve only rather weak security properties, as they may not achieve forward security for the first transmitted payload message and require additional countermeasures against replay attacks.Recently, 0-RTT protocols with full forward security and replay resilience have been proposed in the academic literature. These are based on puncturable encryption, which uses rather heavy building blocks, such as cryptographic pairings. Some constructions were claimed to have practical efficiency, but it is unclear how they compare concretely to protocols deployed in practice, and we currently do not have any benchmark results that new protocols can be compared with.We provide the first concrete performance analysis of a modern 0-RTT protocol with full forward security, by integrating the Bloom Filter Encryption scheme of Derler et al. (EUROCRYPT 2018) in the Chromium QUIC implementation and comparing it to Google’s original QUIC protocol. We find that for reasonable deployment parameters, the server CPU load increases approximately by a factor of eight and the memory consumption on the server increases significantly, but stays below 400 MB even for medium-scale deployments that handle up to 50K connections per day. The difference of the size of handshake messages is small enough that transmission time on the network is identical, and therefore not significant.We conclude that while current 0-RTT protocols with full forward security come with significant computational overhead, their use in practice is feasible, and may be used in applications where the increased CPU and memory load can be tolerated in exchange for full forward security and replay resilience on the cryptographic protocol level. Our results serve as a first benchmark that can be used to assess the efficiency of 0-RTT protocols potentially developed in the future. }}, author = {{Dallmeier, Fynn and Drees, Jan P. and Gellert, Kai and Handirk, Tobias and Jager, Tibor and Klauke, Jonas and Nachtigall, Simon and Renzelmann, Timo and Wolf, Rudi}}, booktitle = {{Cryptology and Network Security}}, isbn = {{9783030654108}}, issn = {{0302-9743}}, location = {{Vienna}}, pages = {{211--231}}, publisher = {{Springer-Verlag}}, title = {{{Forward-Secure 0-RTT Goes Live: Implementation and Performance Analysis in QUIC}}}, doi = {{10.1007/978-3-030-65411-5_11}}, year = {{2020}}, } @inproceedings{20706, author = {{zur Heiden, Philipp}}, booktitle = {{Designing for Digital Transformation. Co-Creating Services with Citizens and Industry}}, isbn = {{9783030648220}}, issn = {{0302-9743}}, title = {{{Considering Context in Design Science Research: A Systematic Literature Review}}}, doi = {{10.1007/978-3-030-64823-7_21}}, year = {{2020}}, } @inproceedings{45848, author = {{Mahmood, Yasir and Meier, Arne}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030399504}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Parameterised Complexity of Model Checking and Satisfiability in Propositional Dependence Logic}}}, doi = {{10.1007/978-3-030-39951-1_10}}, year = {{2020}}, } @inbook{47261, author = {{Haney, Julie M. and Furman, Susanne M. and Acar, Yasemin}}, booktitle = {{HCI for Cybersecurity, Privacy and Trust}}, isbn = {{9783030503086}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Smart Home Security and Privacy Mitigations: Consumer Perceptions, Practices, and Challenges}}}, doi = {{10.1007/978-3-030-50309-3_26}}, year = {{2020}}, } @inbook{11952, author = {{Senft, Björn and Rittmeier, Florian and Fischer, Holger Gerhard and Oberthür, Simon}}, booktitle = {{Design, User Experience, and Usability. Practice and Case Studies}}, isbn = {{9783030235345}}, issn = {{0302-9743}}, location = {{Orlando, FL, USA}}, title = {{{A Value-Centered Approach for Unique and Novel Software Applications}}}, doi = {{10.1007/978-3-030-23535-2_27}}, year = {{2019}}, } @inbook{14890, author = {{Kuhlemann, Stefan and Sellmann, Meinolf and Tierney, Kevin}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030300470}}, issn = {{0302-9743}}, title = {{{Exploiting Counterfactuals for Scalable Stochastic Optimization}}}, doi = {{10.1007/978-3-030-30048-7_40}}, year = {{2019}}, } @inbook{15004, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Discovery Science}}, isbn = {{9783030337773}}, issn = {{0302-9743}}, title = {{{Feature Selection for Analogy-Based Learning to Rank}}}, doi = {{10.1007/978-3-030-33778-0_22}}, year = {{2019}}, } @inbook{15005, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{KI 2019: Advances in Artificial Intelligence}}, isbn = {{9783030301781}}, issn = {{0302-9743}}, title = {{{Analogy-Based Preference Learning with Kernels}}}, doi = {{10.1007/978-3-030-30179-8_3}}, year = {{2019}}, }