@inbook{61765, author = {{Mazur, Andreas and Peters, Henning and Artelt, André and Koller, Lukas and Hartmann, Christoph and Trächtler, Ansgar and Hammer, Barbara}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032045546}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Studying the Generalization Behavior of Surrogate Models for Punch-Bending by Generating Plausible Counterfactuals}}}, doi = {{10.1007/978-3-032-04555-3_16}}, year = {{2025}}, } @inbook{62069, author = {{Kyi, Lin and Santos, Cristiana and Ammanaghatta Shivakumar, Sushil and Roesner, Franziska and Biega, Asia}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032075734}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Turning to Online Forums for Legal Information: A Case Study of GDPR’s Legitimate Interests}}}, doi = {{10.1007/978-3-032-07574-1_7}}, year = {{2025}}, } @inbook{62186, author = {{Jafari, Atousa and Platzner, Marco}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031879944}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Ultra-Low Latency and Extreme-Throughput Echo State Neural Networks on FPGA}}}, doi = {{10.1007/978-3-031-87995-1_11}}, year = {{2025}}, } @inbook{62701, abstract = {{Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.}}, author = {{Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032060655}}, issn = {{0302-9743}}, keywords = {{Decision Tree, OWL Class Expression Learning, Description Logic, Knowledge Graph, Large Language Model, Verbalizer}}, location = {{Porto, Portugal}}, publisher = {{Springer Nature Switzerland}}, title = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}}, doi = {{10.1007/978-3-032-06066-2_29}}, year = {{2025}}, } @inbook{63507, author = {{Pandit, Gaurav and Röder, Michael and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031945748}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Evaluating Approximate Nearest Neighbour Search Systems on Knowledge Graph Embeddings}}}, doi = {{10.1007/978-3-031-94575-5_4}}, year = {{2025}}, } @inproceedings{63572, author = {{Demir, Caglar and Yekini, Moshood Olawale and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032060655}}, issn = {{0302-9743}}, location = {{Porto}}, publisher = {{Springer Nature Switzerland}}, title = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}}, doi = {{10.1007/978-3-032-06066-2_29}}, year = {{2025}}, } @inproceedings{63575, author = {{Kapoor, Sourabh and Sharma, Arnab and Röder, Michael and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031945748}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Robustness Evaluation of Knowledge Graph Embedding Models Under Non-targeted Attacks}}}, doi = {{10.1007/978-3-031-94575-5_15}}, year = {{2025}}, } @inproceedings{63573, author = {{Memariani, Adel and Röder, Michael and Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032095268}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Link Prediction Under Non-targeted Attacks: Do Soft Labels Always Help?}}}, doi = {{10.1007/978-3-032-09527-5_6}}, year = {{2025}}, } @inbook{64881, author = {{Almalki, Nada and Gupta, Siddharth and Michail, Othon and Padalkin, Andreas}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032111265}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Efficient Distributed Algorithms for Shape Reduction via Reconfigurable Circuits}}}, doi = {{10.1007/978-3-032-11127-2_5}}, year = {{2025}}, } @inproceedings{63854, author = {{Eikerling, Hendrik and Kampkötter, Anemone}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031823619}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Enabling Android Application Monitoring by Characterizing Security-Critical Code Fragments}}}, doi = {{10.1007/978-3-031-82362-6_25}}, year = {{2025}}, } @inbook{54412, author = {{Firmansyah, Asep Fajar and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web}}, isbn = {{9783031606250}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{ESLM: Improving Entity Summarization by Leveraging Language Models}}}, doi = {{10.1007/978-3-031-60626-7_9}}, year = {{2024}}, } @inbook{54580, author = {{Mahmood, Yasir and Virtema, Jonni and Barlag, Timon and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031569395}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Computing Repairs Under Functional and Inclusion Dependencies via Argumentation}}}, doi = {{10.1007/978-3-031-56940-1_2}}, year = {{2024}}, } @inbook{54624, author = {{Papenkordt, Jörg}}, booktitle = {{Artificial Intelligence in HCI}}, isbn = {{9783031606052}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Navigating Transparency: The Influence of On-demand Explanations on Non-expert User Interaction with AI}}}, doi = {{10.1007/978-3-031-60606-9_14}}, year = {{2024}}, } @inbook{52759, author = {{Preuß, Oliver Ludger and Rook, Jeroen and Trautmann, Heike}}, booktitle = {{Applications of Evolutionary Computation}}, isbn = {{9783031568510}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems}}}, doi = {{10.1007/978-3-031-56852-7_20}}, year = {{2024}}, } @inbook{54802, abstract = {{Motivated by the prospect of nano-robots that assist human physiological functions at the nanoscale, we investigate the coating problem in the three-dimensional model for hybrid programmable matter. In this model, a single agent with strictly limited viewing range and the computational capability of a deterministic finite automaton can act on passive tiles by picking up a tile, moving, and placing it at some spot. The goal of the coating problem is to fill each node of some surface graph of size n with a tile. We first solve the problem on a restricted class of graphs with a single tile type, and then use constantly many tile types to encode this graph in certain surface graphs capturing the surface of 3D objects. Our algorithm requires O(n^2) steps, which is worst-case optimal compared to an agent with global knowledge and no memory restrictions.}}, author = {{Kostitsyna, Irina and Liedtke, David Jan and Scheideler, Christian}}, booktitle = {{Structural Information and Communication Complexity}}, editor = {{Emek, Yuval}}, isbn = {{9783031606021}}, issn = {{0302-9743}}, keywords = {{Programmable Matter, Coating, Finite Automaton, 3D}}, publisher = {{Springer Nature Switzerland}}, title = {{{Universal Coating by 3D Hybrid Programmable Matter}}}, doi = {{10.1007/978-3-031-60603-8_21}}, year = {{2024}}, } @inbook{56079, author = {{Radoy, Maximilian Manfred and Hebrok, Sven Niclas and Somorovsky, Juraj}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031708954}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{In Search of Partitioning Oracle Attacks Against TLS Session Tickets}}}, doi = {{10.1007/978-3-031-70896-1_16}}, year = {{2024}}, } @inbook{56606, abstract = {{AbstractMost FPGA boards in the HPC domain are well-suited for parallel scaling because of the direct integration of versatile and high-throughput network ports. However, the utilization of their network capabilities is often challenging and error-prone because the whole network stack and communication patterns have to be implemented and managed on the FPGAs. Also, this approach conceptually involves a trade-off between the performance potential of improved communication and the impact of resource consumption for communication infrastructure, since the utilized resources on the FPGAs could otherwise be used for computations. In this work, we investigate this trade-off, firstly, by using synthetic benchmarks to evaluate the different configuration options of the communication framework ACCL and their impact on communication latency and throughput. Finally, we use our findings to implement a shallow water simulation whose scalability heavily depends on low-latency communication. With a suitable configuration of ACCL, good scaling behavior can be shown to all 48 FPGAs installed in the system. Overall, the results show that the availability of inter-FPGA communication frameworks as well as the configurability of framework and network stack are crucial to achieve the best application performance with low latency communication.}}, author = {{Meyer, Marius and Kenter, Tobias and Petrica, Lucian and O’Brien, Kenneth and Blott, Michaela and Plessl, Christian}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031697654}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Optimizing Communication for Latency Sensitive HPC Applications on up to 48 FPGAs Using ACCL}}}, doi = {{10.1007/978-3-031-69766-1_9}}, year = {{2024}}, } @inbook{56581, abstract = {{In recent years, there has been a surge in natural language processing research focused on low-resource languages (LrLs), underscoring the growing recognition that LrLs deserve the same attention as high-resource languages (HrLs). This shift is crucial for ensuring linguistic diversity and inclusivity in the digital age. Despite Indonesian ranking as the 11th most spoken language globally, it remains under-resourced in terms of computational tools and datasets. Within the semantic web domain, Entity Linking (EL) is pivotal, linking textual entity mentions to their corresponding entries in knowledge bases. This process is foundational for advanced information extraction tasks, including relation extraction and event detection. To bolster EL research in Indonesian, we introduce IndEL, the first benchmark dataset tailored for both general and specific domains. IndEL was manually curated using Wikidata, adhering to a rigorous set of annotation guidelines. We used two Named Entity Recognition (NER) benchmark datasets for entity extraction: NER UI for the general domain and IndQNER for the specific domain. IndQNER focused on entities from the Indonesian translation of the Quran. IndEL comprises 4765 entities in the general domain and 2453 in the specific domain. Using the GERBIL framework, we use IndEL to evaluate the performance of various EL systems, such as Babelfy, DBpedia Spotlight, MAG, OpenTapioca, and WAT. Our further investigation reveals that within Wikidata, a significant number of NIL entities remain unlinked due to the limited number of Indonesian labels and the use of acronyms. Especially in the specific domain, transliteration and translation processes performed to create the Indonesian translation of the Quran contribute to the presence of entities in a descriptive form and as synonyms.}}, author = {{Gusmita, Ria Hari and Abshar, Muhammad Faruq Amiral and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031702389}}, issn = {{0302-9743}}, keywords = {{entity linking benchmark dataset, Indonesian, general and specific domains}}, location = {{Turin, Italy}}, publisher = {{Springer Nature Switzerland}}, title = {{{IndEL: Indonesian Entity Linking Benchmark Dataset for General and Specific Domains}}}, doi = {{10.1007/978-3-031-70239-6_34}}, year = {{2024}}, } @inbook{56237, author = {{Schönherr, Johanna and Mayer, Richard E.}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031712906}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Anxiety Moderates the Effects of Drawing Support on Drawing Accuracy in Mathematical Modeling}}}, doi = {{10.1007/978-3-031-71291-3_26}}, year = {{2024}}, } @inbook{64202, author = {{Bora, Revoti Prasad and Terhörst, Philipp and Veldhuis, Raymond and Ramachandra, Raghavendra and Raja, Kiran}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783031781889}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{CoFE: Consistency-Driven Feature Elimination for eXplainable AI}}}, doi = {{10.1007/978-3-031-78189-6_24}}, year = {{2024}}, }