@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}},
}

@inproceedings{55337,
  abstract     = {{As AI is more and more pervasive in everyday life, humans have an increasing demand to understand its behavior and decisions. Most research on explainable AI builds on the premise that there is one ideal explanation to be found. In fact, however, everyday explanations are co-constructed in a dialogue between the person explaining (the explainer) and the specific person being explained to (the explainee). In this paper, we introduce a first corpus of dialogical explanations to enable NLP research on how humans explain as well as on how AI can learn to imitate this process. The corpus consists of 65 transcribed English dialogues from the Wired video series 5 Levels, explaining 13 topics to five explainees of different proficiency. All 1550 dialogue turns have been manually labeled by five independent professionals for the topic discussed as well as for the dialogue act and the explanation move performed. We analyze linguistic patterns of explainers and explainees, and we explore differences across proficiency levels. BERT-based baseline results indicate that sequence information helps predicting topics, acts, and moves effectively.}},
  author       = {{Wachsmuth, Henning and Alshomary, Milad}},
  booktitle    = {{Proceedings of the 29th International Conference on Computational Linguistics}},
  editor       = {{Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon}},
  pages        = {{344–354}},
  publisher    = {{International Committee on Computational Linguistics}},
  title        = {{{“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations}}},
  year         = {{2022}},
}

@inproceedings{34067,
  author       = {{Sengupta, Meghdut and Alshomary, Milad and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 2022 Workshop on Figurative Language Processing}},
  title        = {{{Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning}}},
  year         = {{2022}},
}

@inproceedings{35674,
  abstract     = {{<jats:p>We report on our work with students in our data science courses, focusing on the analysis of students’ results. This study represents an in-depth analysis of students’ creation and documentation of machine learning models. The students were supported by educationally designed Jupyter Notebooks, which are used as worked examples. Using the worked example, students document their results in a so-called computational essay. We examine which aspects of creating computational essays are difficult for students to find out how worked examples should be designed to support students without being too prescriptive. We analyze the computational essays produced by students and draw consequences for redesigning our worked example.</jats:p>}},
  author       = {{Fleischer, Franz Yannik and Hüsing, Sven and Biehler, Rolf and Podworny, Susanne and Schulte, Carsten}},
  booktitle    = {{Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics}},
  editor       = {{Peters, S. A. and Zapata-Cardona, L. and Bonafini, F. and Fan, A.}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Jupyter Notebooks for Teaching, Learning, and Doing Data Science}}},
  doi          = {{10.52041/iase.icots11.t10e3}},
  year         = {{2022}},
}

@article{35672,
  abstract     = {{<jats:p>This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.</jats:p>}},
  author       = {{Fleischer, Franz Yannik and Biehler, Rolf and Schulte, Carsten}},
  issn         = {{1570-1824}},
  journal      = {{Statistics Education Research Journal}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks}}},
  doi          = {{10.52041/serj.v21i2.61}},
  volume       = {{21}},
  year         = {{2022}},
}

@article{34716,
  author       = {{Terhörst, Philipp and Kolf, Jan Niklas and Huber, Marco and Kirchbuchner, Florian and Damer, Naser and Moreno, Aythami Morales and Fierrez, Julian and Kuijper, Arjan}},
  journal      = {{IEEE Transactions on Technology and Society}},
  number       = {{1}},
  pages        = {{16--30}},
  title        = {{{A Comprehensive Study on Face Recognition Biases Beyond Demographics}}},
  doi          = {{10.1109/TTS.2021.3111823}},
  volume       = {{3}},
  year         = {{2022}},
}

@inproceedings{34710,
  author       = {{Huber, Marco and Terhörst, Philipp and Luu, Anh Thi and Kirchbuchner, Florian and Damer, Naser}},
  booktitle    = {{26th International Conference on Pattern Recognition, ICPR 2022, Montreal, QC, Canada, August 21-25, 2022}},
  pages        = {{938–944}},
  publisher    = {{IEEE}},
  title        = {{{Verification of Sitter Identity Across Historical Portrait Paintings by Confidence-aware Face Recognition}}},
  doi          = {{10.1109/ICPR56361.2022.9956452}},
  year         = {{2022}},
}