@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}}, } @inbook{41792, author = {{Fuchs, Christian}}, booktitle = {{Soziale Ordnungen des Sterbens}}, editor = {{von Gahlen-Hoops, Wolfgang}}, publisher = {{transcript Verlag}}, title = {{{Der Tod und die Liebe: Die Metaphysik der Kommunikation}}}, year = {{2022}}, } @article{33256, author = {{Fuchs, Christian and Unterberger, Klaus}}, journal = {{Public Value Texte}}, pages = {{54--63}}, title = {{{Transform the Internet: Why we Need a Public Service Internet}}}, volume = {{25}}, year = {{2022}}, } @inbook{42390, author = {{Fuchs, Christian}}, booktitle = {{Η νέα ψηφιακή παρακολούθηση. Κείμενα προς τιμήν του Μηνά Σαματά (in Greek, The New Digital Surveillance: A Volume in Honour of Minas Samatas),}}, editor = {{Theoharis, Yannis and Kaniadakis, Antonis}}, pages = {{149--171}}, publisher = {{Papazisis Publishers}}, title = {{{H παρακολούθηση των Κοινωνικών Μέσων}}}, year = {{2022}}, } @inbook{41794, author = {{Unterberger, Klaus and Fuchs, Christian}}, booktitle = {{Building a European Digital Public Space: Strategies for Taking Back Control from Big Tech Platforms}}, editor = {{Baratsits, Alexander}}, pages = {{243--256}}, publisher = {{iRights.Media}}, title = {{{Occupy the Internet: Why we need a Public Service Internet}}}, year = {{2022}}, } @inbook{41793, author = {{Fuchs, Christian}}, booktitle = {{The Social Media Debate: Unpacking the Social, Psychological, and Cultural Effects of Social Media}}, editor = {{Rosen, Devan}}, pages = {{53--76}}, publisher = {{Routledge}}, title = {{{Social Media, Alienation, and the Public Sphere}}}, year = {{2022}}, } @book{37188, author = {{Fuchs, Christian}}, isbn = {{9781803824222}}, publisher = {{Emerald}}, title = {{{Digital Humanism. A Philosophy for 21st Century Digital Society. SocietyNow Series}}}, year = {{2022}}, } @article{41798, author = {{Fuchs, Christian}}, journal = {{国际社会科学杂志 (Journal of International Social Sciences) 2022 (5)}}, pages = {{31--42}}, title = {{{大数据、社交媒体和数字异化}}}, year = {{2022}}, } @article{42482, author = {{Fuchs, Christian}}, journal = {{Publizistik}}, number = {{1}}, pages = {{128--129}}, title = {{{Geburtstage: Manfred Knoche 80 Jahre}}}, volume = {{67}}, year = {{2022}}, } @book{30283, author = {{Fuchs, Christian}}, publisher = {{UVK/utb}}, title = {{{Verschwörungstheorien in der Pandemie}}}, year = {{2022}}, } @article{42481, author = {{Fuchs, Christian}}, journal = {{Wired Italia 102}}, pages = {{31}}, title = {{{Servizio Pubblico}}}, year = {{2022}}, } @inbook{30285, author = {{Fuchs, Christian}}, booktitle = {{The Social Media Debate: Unpacking the Social, Psychological, and Cultural Effects of Social Media}}, editor = {{Rosen, Devan}}, pages = {{53--76}}, publisher = {{Routledge}}, title = {{{Social Media, Alienation, and the Public Sphere}}}, year = {{2022}}, } @inbook{38151, author = {{Fuchs, Christian}}, booktitle = {{Searching for a Self. Identity in Popular Culture, Media and Society}}, editor = {{Berger, Arthur Asa}}, pages = {{68--71}}, publisher = {{Vernon Press}}, title = {{{Marxism and Identity}}}, year = {{2022}}, } @article{41546, author = {{Fuchs, Christian}}, journal = {{国际社会科学杂志 (Journal of International Social Sciences)}}, pages = {{47--62}}, title = {{{交往资本主义：迈向马克思主义交往理论的批判}}}, volume = {{4}}, year = {{2022}}, } @article{41590, author = {{Fuchs, Christian}}, journal = {{ 国际社会科学杂志 (Journal of International Social Sciences) 2022 (4)}}, pages = {{28--46}}, title = {{{社交媒体中的数字产消劳动：基于资本主义时间范畴的研究}}}, year = {{2022}}, } @article{41540, author = {{Fuchs, Christian}}, journal = {{马克思主义与现实 (Marxism & Reality) 年第5期. (shortened article version)}}, pages = {{194--202}}, title = {{{数字资本主义和大数据时代的卢卡奇}}}, year = {{2022}}, }