@inproceedings{47842,
  author       = {{Fourné, Marcel and Wermke, Dominik and Enck, William and Fahl, Sascha and Acar, Yasemin}},
  booktitle    = {{2023 IEEE Symposium on Security and Privacy (SP)}},
  publisher    = {{IEEE}},
  title        = {{{It’s like flossing your teeth: On the Importance and Challenges of Reproducible Builds for Software Supply Chain Security}}},
  doi          = {{10.1109/sp46215.2023.10179320}},
  year         = {{2023}},
}

@article{47295,
  author       = {{Amft, Sabrina and Höltervennhoff, Sandra and Huaman, Nicolas and Krause, Alexander and Simko, Lucy and Acar, Yasemin and Fahl, Sascha}},
  journal      = {{CoRR}},
  title        = {{{Lost and not Found: An Investigation of Recovery Methods for Multi-Factor Authentication}}},
  doi          = {{10.48550/arXiv.2306.09708}},
  volume       = {{abs/2306.09708}},
  year         = {{2023}},
}

@article{47294,
  author       = {{Tran, Mindy and Acar, Yasemin and Cucker, Michel and Enck, William and Kapravelos, Alexandros and Kästner, Christian and Williams, Laurie A.}},
  journal      = {{CoRR}},
  title        = {{{S3C2 Summit 2202-09: Industry Secure Suppy Chain Summit}}},
  doi          = {{10.48550/arXiv.2307.15642}},
  volume       = {{abs/2307.15642}},
  year         = {{2023}},
}

@article{47293,
  author       = {{Dunlap, Trevor and Acar, Yasemin and Cucker, Michel and Enck, William and Kapravelos, Alexandros and Kästner, Christian and Williams, Laurie A.}},
  journal      = {{CoRR}},
  title        = {{{S3C2 Summit 2023-02: Industry Secure Supply Chain Summit}}},
  doi          = {{10.48550/arXiv.2307.16557}},
  volume       = {{abs/2307.16557}},
  year         = {{2023}},
}

@article{47292,
  author       = {{Enck, William and Acar, Yasemin and Cukier, Michel and Kapravelos, Alexandros and Kästner, Christian and Williams, Laurie A.}},
  journal      = {{CoRR}},
  title        = {{{S3C2 Summit 2023-06: Government Secure Supply Chain Summit}}},
  doi          = {{10.48550/arXiv.2308.06850}},
  volume       = {{abs/2308.06850}},
  year         = {{2023}},
}

@inproceedings{47300,
  author       = {{Kohno, Tadayoshi and Acar, Yasemin and Loh, Wulf}},
  booktitle    = {{32nd USENIX Security Symposium, USENIX Security 2023, Anaheim, CA, USA, August 9-11, 2023}},
  editor       = {{Calandrino, Joseph A. and Troncoso, Carmela}},
  publisher    = {{USENIX Association}},
  title        = {{{Ethical Frameworks and Computer Security Trolley Problems: Foundations for Conversations}}},
  year         = {{2023}},
}

@inproceedings{47312,
  author       = {{Neil, Lorenzo and Sri Ramulu, Harshini and Acar, Yasemin and Reaves, Bradley}},
  booktitle    = {{Nineteenth Symposium on Usable Privacy and Security, SOUPS 2023, Anaheim, CA, USA, August 5-7, 2023}},
  pages        = {{283–299}},
  publisher    = {{USENIX Association}},
  title        = {{{Who Comes Up with this Stuff? Interviewing Authors to Understand How They Produce Security Advice}}},
  year         = {{2023}},
}

@article{47291,
  author       = {{Klemmer, Jan H. and Gutfleisch, Marco and Stransky, Christian and Acar, Yasemin and Sasse, M. Angela and Fahl, Sascha}},
  journal      = {{CoRR}},
  title        = {{{"Make Them Change it Every Week!": A Qualitative Exploration of Online Developer Advice on Usable and Secure Authentication}}},
  doi          = {{10.48550/arXiv.2309.00744}},
  volume       = {{abs/2309.00744}},
  year         = {{2023}},
}

@inproceedings{53362,
  author       = {{Amft, Sabrina and Höltervennhoff, Sandra and Huaman, Nicolas and Krause, Alexander and Simko, Lucy and Acar, Yasemin and Fahl, Sascha}},
  booktitle    = {{Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, CCS 2023, Copenhagen, Denmark, November 26-30, 2023}},
  editor       = {{Meng, Weizhi and Jensen, Christian Damsgaard and Cremers, Cas and Kirda, Engin}},
  pages        = {{3138–3152}},
  publisher    = {{ACM}},
  title        = {{{"We’ve Disabled MFA for You": An Evaluation of the Security and Usability of Multi-Factor Authentication Recovery Deployments}}},
  doi          = {{10.1145/3576915.3623180}},
  year         = {{2023}},
}

@inproceedings{49438,
  author       = {{Krüger, Stefan and Reif, Michael and Wickert, Anna-Katharina and Nadi, Sarah and Ali, Karim and Bodden, Eric and Acar, Yasemin and Mezini, Mira and Fahl, Sascha}},
  booktitle    = {{2023 IEEE Secure Development Conference (SecDev)}},
  publisher    = {{IEEE}},
  title        = {{{Securing Your Crypto-API Usage Through Tool Support - A Usability Study}}},
  doi          = {{10.1109/secdev56634.2023.00015}},
  year         = {{2023}},
}

@article{53368,
  author       = {{Fourné, Marcel and Wermke, Dominik and Fahl, Sascha and Acar, Yasemin}},
  journal      = {{IEEE Security & Privacy}},
  number       = {{6}},
  pages        = {{59–63}},
  publisher    = {{IEEE}},
  title        = {{{A Viewpoint on Human Factors in Software Supply Chain Security: A Research Agenda}}},
  volume       = {{21}},
  year         = {{2023}},
}

@inproceedings{53366,
  author       = {{Tran, Mindy and Munyendo, Collins W and Sri Ramulu, Harshini and Rodriguez, Rachel Gonzalez and Schnell, Luisa Ball and Sula, Cora and Simko, Lucy and Acar, Yasemin}},
  booktitle    = {{2024 IEEE Symposium on Security and Privacy (SP)}},
  pages        = {{4–4}},
  title        = {{{Security, Privacy, and Data-sharing Trade-offs When Moving to the United States: Insights from a Qualitative Study}}},
  year         = {{2023}},
}

@article{53348,
  author       = {{Fourné, Marcel and Wermke, Dominik and Fahl, Sascha and Acar, Yasemin}},
  journal      = {{IEEE Secur. Priv.}},
  number       = {{6}},
  pages        = {{59–63}},
  title        = {{{A Viewpoint on Human Factors in Software Supply Chain Security: A Research Agenda}}},
  doi          = {{10.1109/MSEC.2023.3316569}},
  volume       = {{21}},
  year         = {{2023}},
}

@article{53352,
  author       = {{Simko, Lucy and Sri Ramulu, Harshini and Kohno, Tadayoshi and Acar, Yasemin}},
  journal      = {{Proc. ACM Hum. Comput. Interact.}},
  number       = {{CSCW2}},
  pages        = {{1–54}},
  title        = {{{The Use and Non-Use of Technology During Hurricanes}}},
  doi          = {{10.1145/3610215}},
  volume       = {{7}},
  year         = {{2023}},
}

@article{48589,
  author       = {{Fuchs, Christian}},
  journal      = {{Critical Sociology}},
  number       = {{4-5}},
  pages        = {{727--745}},
  title        = {{{Ibn Khaldûn and the Political Economy of Communication in the Age of Digital Capitalism}}},
  doi          = {{10.1177/08969205231206488 }},
  volume       = {{50}},
  year         = {{2023}},
}

@article{48590,
  author       = {{Fuchs, Christian}},
  journal      = {{Critical Sociology}},
  number       = {{4-5}},
  pages        = {{757--765}},
  title        = {{{Ibn Khaldûn and the Political Economy of Communication: A Reply to Graham Murdock}}},
  doi          = {{10.1177/08969205231201382}},
  volume       = {{50}},
  year         = {{2023}},
}

@article{46310,
  abstract     = {{Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) nearest neighbor relationships of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-nearest neighbor graphs (NNG) of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. A proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.}},
  author       = {{Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}},
  issn         = {{0304-3975}},
  journal      = {{Theoretical Computer Science}},
  keywords     = {{Feature normalization, Algorithm selection, Traveling salesperson problem}},
  pages        = {{123--145}},
  title        = {{{A study on the effects of normalized TSP features for automated algorithm selection}}},
  doi          = {{https://doi.org/10.1016/j.tcs.2022.10.019}},
  volume       = {{940}},
  year         = {{2023}},
}

@inproceedings{48898,
  abstract     = {{Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin\textendash Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100000 TSP instances with 2000 nodes each \textemdash currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets.}},
  author       = {{Seiler, Moritz and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver Ludger and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{2023 IEEE Symposium Series on Computational Intelligence (SSCI)}},
  pages        = {{361 -- 368}},
  title        = {{{Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP}}},
  doi          = {{10.1109/SSCI52147.2023.10372008}},
  year         = {{2023}},
}

@inbook{54672,
  author       = {{Schmelter, David and Steghöfer, Jan-Philipp and Albers, Karsten and Ekman, Mats and Tessmer, Jörg and Weber, Raphael}},
  booktitle    = {{Communications in Computer and Information Science}},
  isbn         = {{9783031423062}},
  issn         = {{1865-0929}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Trustful Model-Based Information Exchange in Collaborative Engineering}}},
  doi          = {{10.1007/978-3-031-42307-9_12}},
  year         = {{2023}},
}

@inbook{47548,
  author       = {{Fuchs, Christian}},
  booktitle    = {{Eigentum, Medien, Öffentlichkeit: Verhandlungen des Netzwerks Kritische Kommunikationswissenschaft}},
  editor       = {{Güney, Selma and Hille, Lina and Pfeiffer, Juliane  and Porak, Laura and Theine, Hendrik}},
  pages        = {{215--236}},
  publisher    = {{Westend Verlag}},
  title        = {{{Zur Kritik der Politischen Ökonomie des Digitalen Kapitalismus: Die Aktualität von Manfred Knoches Beitrag zur Kritik der Politischen Ökonomie der Medien und der Kommunikation}}},
  doi          = {{https://doi.org/10.53291/BWUB5365}},
  year         = {{2023}},
}

