@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}}, } @inproceedings{47286, author = {{Gutfleisch, Marco and Klemmer, Jan H. and Busch, Niklas and Acar, Yasemin and Sasse, M. Angela and Fahl, Sascha}}, booktitle = {{43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022}}, pages = {{893–910}}, publisher = {{IEEE}}, title = {{{How Does Usable Security (Not) End Up in Software Products? Results From a Qualitative Interview Study}}}, doi = {{10.1109/SP46214.2022.9833756}}, year = {{2022}}, } @inproceedings{47287, author = {{Stransky, Christian and Wiese, Oliver and Roth, Volker and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022}}, pages = {{860–875}}, publisher = {{IEEE}}, title = {{{27 Years and 81 Million Opportunities Later: Investigating the Use of Email Encryption for an Entire University}}}, doi = {{10.1109/SP46214.2022.9833755}}, year = {{2022}}, } @inproceedings{47283, author = {{Kaur, Harjot and Amft, Sabrina and Votipka, Daniel and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{31st USENIX Security Symposium, USENIX Security 2022, Boston, MA, USA, August 10-12, 2022}}, editor = {{Butler, Kevin R. B. and Thomas, Kurt}}, pages = {{4041–4058}}, publisher = {{USENIX Association}}, title = {{{Where to Recruit for Security Development Studies: Comparing Six Software Developer Samples}}}, year = {{2022}}, } @article{47290, author = {{Huaman, Nicolas and Amft, Sabrina and Oltrogge, Marten and Acar, Yasemin and Fahl, Sascha}}, journal = {{IEEE Secur. Priv.}}, number = {{2}}, pages = {{49–60}}, title = {{{They Would Do Better If They Worked Together: Interaction Problems Between Password Managers and the Web}}}, doi = {{10.1109/MSEC.2021.3123795}}, volume = {{20}}, year = {{2022}}, } @inproceedings{47843, author = {{Wermke, Dominik and Wohler, Noah and Klemmer, Jan H. and Fourné, Marcel and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{2022 IEEE Symposium on Security and Privacy (SP)}}, publisher = {{IEEE}}, title = {{{Committed to Trust: A Qualitative Study on Security & Trust in Open Source Software Projects}}}, doi = {{10.1109/sp46214.2022.9833686}}, year = {{2022}}, } @inproceedings{47288, 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 = {{43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022}}, pages = {{632–649}}, 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}}, } @inproceedings{47285, author = {{Wermke, Dominik and Wöhler, Noah and Klemmer, Jan H. and Fourné, Marcel and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022}}, pages = {{1880–1896}}, publisher = {{IEEE}}, title = {{{Committed to Trust: A Qualitative Study on Security & Trust in Open Source Software Projects}}}, doi = {{10.1109/SP46214.2022.9833686}}, year = {{2022}}, } @inproceedings{47284, author = {{Munyendo, Collins W. and Acar, Yasemin and Aviv, Adam J.}}, booktitle = {{43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022}}, pages = {{2304–2319}}, publisher = {{IEEE}}, title = {{{"Desperate Times Call for Desperate Measures": User Concerns with Mobile Loan Apps in Kenya}}}, doi = {{10.1109/SP46214.2022.9833779}}, year = {{2022}}, } @article{47281, author = {{Krause, Alexander and Klemmer, Jan H. and Huaman, Nicolas and Wermke, Dominik and Acar, Yasemin and Fahl, Sascha}}, journal = {{CoRR}}, title = {{{Committed by Accident: Studying Prevention and Remediation Strategies Against Secret Leakage in Source Code Repositories}}}, doi = {{10.48550/arXiv.2211.06213}}, volume = {{abs/2211.06213}}, year = {{2022}}, } @inproceedings{46307, abstract = {{Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection.}}, author = {{Seiler, Moritz and Prager, Raphael Patrick and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{9781450392372}}, pages = {{657–665}}, publisher = {{Association for Computing Machinery}}, title = {{{A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes}}}, doi = {{10.1145/3512290.3528834}}, year = {{2022}}, } @inproceedings{46304, abstract = {{In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.}}, author = {{Prager, Raphael Patrick and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}}, booktitle = {{Parallel Problem Solving from Nature — PPSN XVII}}, editor = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tušar, Tea}}, isbn = {{978-3-031-14714-2}}, pages = {{3–17}}, publisher = {{Springer International Publishing}}, title = {{{Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods}}}, doi = {{10.1007/978-3-031-14714-2_1}}, year = {{2022}}, } @inproceedings{46303, abstract = {{Social media platforms are essential for information sharing and, thus, prone to coordinated dis- and misinformation campaigns. Nevertheless, research in this area is hampered by strict data sharing regulations imposed by the platforms, resulting in a lack of benchmark data. Previous work focused on circumventing these rules by either pseudonymizing the data or sharing fragments. In this work, we will address the benchmarking crisis by presenting a methodology that can be used to create artificial campaigns out of original campaign building blocks. We conduct a proof-of-concept study using the freely available generative language model GPT-Neo in this context and demonstrate that the campaign patterns can flexibly be adapted to an underlying social media stream and evade state-of-the-art campaign detection approaches based on stream clustering. Thus, we not only provide a framework for artificial benchmark generation but also demonstrate the possible adversarial nature of such benchmarks for challenging and advancing current campaign detection methods.}}, author = {{Pohl, Janina Susanne and Assenmacher, Dennis and Seiler, Moritz and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)}}, editor = {{the Advancement of Artificial Intelligence (AAAI) Association, for}}, pages = {{1–10}}, publisher = {{AAAI Press}}, title = {{{Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches}}}, doi = {{10.36190/2022.91}}, year = {{2022}}, } @article{46309, 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—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}}, journal = {{Applied Sciences}}, number = {{8}}, pages = {{1–44}}, title = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}}, doi = {{10.3390/app12189094}}, volume = {{12}}, year = {{2022}}, } @inproceedings{46302, author = {{Heins, J and Rook, J and Schäpermeier, L and Kerschke, P and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Parallel Problem Solving from Nature — PPSN XVII}}, editor = {{Rudolph, G and Kononova, AV and Aguirre, H and Kerschke, P and Ochoa, G and Tušar, T}}, isbn = {{978-3-031-14714-2}}, pages = {{192–206}}, publisher = {{Springer International Publishing}}, title = {{{BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}}}, year = {{2022}}, }