@article{48878, abstract = {{Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.}}, author = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{2076-3417}}, journal = {{Applied Sciences}}, keywords = {{big data, data mining, data stream analysis, machine learning, stream classification, supervised learning}}, number = {{18}}, pages = {{9094}}, publisher = {{{Multidisciplinary Digital Publishing Institute}}}, title = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}}, doi = {{10.3390/app12189094}}, volume = {{12}}, year = {{2022}}, } @inproceedings{48896, abstract = {{Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.}}, author = {{Rook, Jeroen and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-9268-6}}, keywords = {{configuration, multi-modality, multi-objective optimization}}, pages = {{356–359}}, publisher = {{Association for Computing Machinery}}, title = {{{On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}}}, doi = {{10.1145/3520304.3528998}}, year = {{2022}}, } @article{52532, author = {{Rodrigues, Agatha S. and Kerschke, Pascal and Pereira, Carlos Alberto De Bragança and Trautmann, Heike and Wagner, Carolin and Hellingrath, Bernd and Polpo, Adriano}}, journal = {{Comput. Stat.}}, number = {{1}}, pages = {{355–379}}, title = {{{Estimation of component reliability from superposed renewal processes by means of latent variables}}}, doi = {{10.1007/S00180-021-01124-0}}, volume = {{37}}, year = {{2022}}, } @article{52862, author = {{Turhan, Anni-Yasmin}}, issn = {{0933-1875}}, journal = {{KI - Künstliche Intelligenz}}, keywords = {{Artificial Intelligence}}, number = {{1}}, pages = {{1--4}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A Double Take at Conferences: The Hybrid Format}}}, doi = {{10.1007/s13218-022-00758-6}}, volume = {{36}}, year = {{2022}}, } @inproceedings{52923, author = {{de Camargo e Souza Câmara, Igor and Turhan, Anni-Yasmin}}, booktitle = {{Proceedings of the 20th International Workshop on Non-Monotonic Reasoning, NMR 2022, Part of the Federated Logic Conference (FLoC 2022), Haifa, Israel, August 7-9, 2022}}, editor = {{Arieli, Ofer and Casini, Giovanni and Giordano, Laura}}, pages = {{159–162}}, publisher = {{CEUR-WS.org}}, title = {{{Rational Defeasible Subsumption in DLs with Nested Quantifiers: the Case of ELI\(\perp\)}}}, volume = {{3197}}, year = {{2022}}, } @proceedings{52925, editor = {{Governatori, Guido and Turhan, Anni-Yasmin}}, isbn = {{978-3-031-21540-7}}, publisher = {{Springer}}, title = {{{Rules and Reasoning - 6th International Joint Conference on Rules and Reasoning, RuleML+RR 2022, Berlin, Germany, September 26-28, 2022, Proceedings}}}, doi = {{10.1007/978-3-031-21541-4}}, volume = {{13752}}, year = {{2022}}, } @inproceedings{32572, author = {{Mayer, Peter and Poddebniak, Damian and Fischer, Konstantin and Brinkmann, Marcus and Somorovsky, Juraj and Sasse, Angela and Schinzel, Sebastian and Volkamer, Melanie}}, booktitle = {{Eighteenth Symposium on Usable Privacy and Security (SOUPS 2022)}}, isbn = {{978-1-939133-30-4}}, pages = {{77–96}}, publisher = {{USENIX Association}}, title = {{{"I don' know why I check this..." - Investigating Expert Users' Strategies to Detect Email Signature Spoofing Attacks}}}, year = {{2022}}, } @inproceedings{32573, author = {{Maehren, Marcel and Nieting, Philipp and Hebrok, Sven Niclas and Merget, Robert and Somorovsky, Juraj and Schwenk, Jörg}}, booktitle = {{31st USENIX Security Symposium (USENIX Security 22)}}, publisher = {{USENIX Association}}, title = {{{TLS-Anvil: Adapting Combinatorial Testing for TLS Libraries}}}, 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}}, } @inproceedings{47831, author = {{Ködding, Patrick and Dumitrescu, Roman}}, booktitle = {{Digitalisierung souverän gestalten II}}, editor = {{Hartmann, Ernst A.}}, title = {{{Szenario-Technik mit digitalen Technologien}}}, year = {{2022}}, } @article{53952, author = {{Massacci, Fabio and Sabetta, Antonino and Mirkovic, Jelena and Murray, Toby and Okhravi, Hamed and Mannan, Mohammad and Rocha, Anderson and Bodden, Eric and Geer, Daniel E.}}, issn = {{1540-7993}}, journal = {{IEEE Security & Privacy}}, number = {{5}}, pages = {{16--21}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{“Free” as in Freedom to Protest?}}}, doi = {{10.1109/msec.2022.3185845}}, volume = {{20}}, year = {{2022}}, } @inproceedings{54435, abstract = {{Web browsers are among the most important but also complex software solutions to access the web. It is therefore not surprising that web browsers are an attractive target for attackers. Especially in the last decade, security researchers and browser vendors have developed sandboxing mechanisms like security-relevant HTTP headers to tackle the problem of getting a more secure browser. Although the security community is aware of the importance of security-relevant HTTP headers, legacy applications and individual requests from different parties have led to possible insecure configurations of these headers. Even if specific security headers are configured correctly, conflicts in their functionalities may lead to unforeseen browser behaviors and vulnerabilities. Recently, the first work which analyzed duplicated headers and conflicts in headers was published by Calzavara et al. at USENIX Security [1]. The authors focused on inconsistent protections by using both, the HTTP header X-Frame-Options and the framing protection of the Content-Security-Policy. We extend their work by analyzing browser behaviors when parsing duplicated headers, conflicting directives, and values that do not conform to the defined ABNF metalanguage specification. We created an open-source testbed running over 19,800 test cases, at which nearly 300 test cases are executed in the set of 66 different browsers. Our work shows that browsers conform to the specification and behave securely. However, all tested browsers behave differently when it comes, for example, to parsing the Strict-Transport-Security header. Moreover, Chrome, Safari, and Firefox behave differently if the header contains a character, which is not allowed by the defined ABNF. This results in the protection mechanism being fully enforced, partially enforced, or not enforced and thus completely bypassable.}}, author = {{Siewert, Hendrik and Kretschmer, Martin and Niemietz, Marcus and Somorovsky, Juraj}}, booktitle = {{2022 IEEE Security and Privacy Workshops (SPW)}}, publisher = {{IEEE}}, title = {{{On the Security of Parsing Security-Relevant HTTP Headers in Modern Browsers}}}, doi = {{10.1109/spw54247.2022.9833880}}, year = {{2022}}, } @inproceedings{29290, abstract = {{Classifying nodes in knowledge graphs is an important task, e.g., predicting missing types of entities, predicting which molecules cause cancer, or predicting which drugs are promising treatment candidates. While black-box models often achieve high predictive performance, they are only post-hoc and locally explainable and do not allow the learned model to be easily enriched with domain knowledge. Towards this end, learning description logic concepts from positive and negative examples has been proposed. However, learning such concepts often takes a long time and state-of-the-art approaches provide limited support for literal data values, although they are crucial for many applications. In this paper, we propose EvoLearner - an evolutionary approach to learn ALCQ(D), which is the attributive language with complement (ALC) paired with qualified cardinality restrictions (Q) and data properties (D). We contribute a novel initialization method for the initial population: starting from positive examples (nodes in the knowledge graph), we perform biased random walks and translate them to description logic concepts. Moreover, we improve support for data properties by maximizing information gain when deciding where to split the data. We show that our approach significantly outperforms the state of the art on the benchmarking framework SML-Bench for structured machine learning. Our ablation study confirms that this is due to our novel initialization method and support for data properties.}}, author = {{Heindorf, Stefan and Blübaum, Lukas and Düsterhus, Nick and Werner, Till and Golani, Varun Nandkumar and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{WWW}}, pages = {{818--828}}, publisher = {{ACM}}, title = {{{EvoLearner: Learning Description Logics with Evolutionary Algorithms}}}, doi = {{10.1145/3485447.3511925}}, year = {{2022}}, } @article{29851, author = {{Pestryakova, Svetlana and Vollmers, Daniel and Sherif, Mohamed and Heindorf, Stefan and Saleem, Muhammad and Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}}, journal = {{Scientific Data}}, title = {{{CovidPubGraph: A FAIR Knowledge Graph of COVID-19 Publications}}}, doi = {{10.1038/s41597-022-01298-2}}, year = {{2022}}, } @inproceedings{34674, abstract = {{Smart home systems contain plenty of features that enhance wellbeing in everyday life through artificial intelligence (AI). However, many users feel insecure because they do not understand the AI’s functionality and do not feel they are in control of it. Combining technical, psychological and philosophical views on AI, we rethink smart homes as interactive systems where users can partake in an intelligent agent’s learning. Parallel to the goals of explainable AI (XAI), we explored the possibility of user involvement in supervised learning of the smart home to have a first approach to improve acceptance, support subjective understanding and increase perceived control. In this work, we conducted two studies: In an online pre-study, we asked participants about their attitude towards teaching AI via a questionnaire. In the main study, we performed a Wizard of Oz laboratory experiment with human participants, where participants spent time in a prototypical smart home and taught activity recognition to the intelligent agent through supervised learning based on the user’s behaviour. We found that involvement in the AI’s learning phase enhanced the users’ feeling of control, perceived understanding and perceived usefulness of AI in general. The participants reported positive attitudes towards training a smart home AI and found the process understandable and controllable. We suggest that involving the user in the learning phase could lead to better personalisation and increased understanding and control by users of intelligent agents for smart home automation.}}, author = {{Sieger, Leonie Nora and Hermann, Julia and Schomäcker, Astrid and Heindorf, Stefan and Meske, Christian and Hey, Celine-Chiara and Doğangün, Ayşegül}}, booktitle = {{International Conference on Human-Agent Interaction}}, keywords = {{human-agent interaction, smart homes, supervised learning, participation}}, location = {{Christchurch, New Zealand}}, publisher = {{ACM}}, title = {{{User Involvement in Training Smart Home Agents}}}, doi = {{10.1145/3527188.3561914}}, year = {{2022}}, } @inproceedings{52924, author = {{Tirtarasa, Satyadharma and Turhan, Anni-Yasmin}}, booktitle = {{SAC ’22: The 37th {ACM/SIGAPP} Symposium on Applied Computing, Virtual Event, April 25 - 29, 2022}}, editor = {{Hong, Jiman and Bures, Miroslav and Park, Juw Won and Cerný, Tomás}}, pages = {{903–910}}, publisher = {{ACM}}, title = {{{Computing generalizations of temporal ϵL concepts with next and global}}}, doi = {{10.1145/3477314.3507136}}, year = {{2022}}, } @article{52918, author = {{Baader, Franz and Koopmann, Patrick and Michel, Friedrich and Turhan, Anni-Yasmin and Zarrieß, Benjamin}}, journal = {{Theory Pract. Log. Program.}}, number = {{2}}, pages = {{162–192}}, title = {{{Efficient TBox Reasoning with Value Restrictions using the Flower Reasoner}}}, doi = {{10.1017/S1471068421000466}}, volume = {{22}}, year = {{2022}}, } @inbook{54585, author = {{Manzoor, Ali and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web – ISWC 2022}}, isbn = {{9783031194320}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{REBench: Microbenchmarking Framework for Relation Extraction Systems}}}, doi = {{10.1007/978-3-031-19433-7_37}}, year = {{2022}}, } @inproceedings{47289, author = {{Huaman, Nicolas and Krause, Alexander and Wermke, Dominik and Klemmer, Jan H. and Stransky, Christian and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{Eighteenth Symposium on Usable Privacy and Security, SOUPS 2022, Boston, MA, USA, August 7-9, 2022}}, editor = {{Chiasson, Sonia and Kapadia, Apu}}, pages = {{313–330}}, publisher = {{USENIX Association}}, title = {{{If You Can’t Get Them to the Lab: Evaluating a Virtual Study Environment with Security Information Workers}}}, year = {{2022}}, } @inproceedings{47844, author = {{Jancar, Jan and Fourné, Marcel and Braga, Daniel De Almeida and Sabt, Mohamed and Schwabe, Peter and Barthe, Gilles and Fouque, Pierre-Alain and Acar, Yasemin}}, booktitle = {{2022 IEEE Symposium on Security and Privacy (SP)}}, publisher = {{IEEE}}, title = {{{“They’re not that hard to mitigate”: What Cryptographic Library Developers Think About Timing Attacks}}}, doi = {{10.1109/sp46214.2022.9833713}}, year = {{2022}}, }