@misc{34963, author = {{Anonymous, A}}, title = {{{Cost of Privacy-preserving SMPC Protocols for NN-Based Inference}}}, year = {{2022}}, } @misc{35772, author = {{Lohse, Jan}}, title = {{{Lattice Revocation Mechanisms}}}, year = {{2022}}, } @techreport{36227, author = {{Hammer, Barbara and Hüllermeier, Eyke and Lohweg, Volker and Schneider, Alexander and Schenck, Wolfram and Kuhl, Ulrike and Braun, Marco and Pfeifer, Anton and Holst, Christoph-Alexander and Schmidt, Malte and Schomaker, Gunnar and Tornede, Tanja}}, title = {{{Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens}}}, doi = {{10.4119/unibi/2965622}}, year = {{2022}}, } @inproceedings{27531, abstract = {{The Quantum Singular Value Transformation (QSVT) is a recent technique that gives a unified framework to describe most quantum algorithms discovered so far, and may lead to the development of novel quantum algorithms. In this paper we investigate the hardness of classically simulating the QSVT. A recent result by Chia, Gily\'en, Li, Lin, Tang and Wang (STOC 2020) showed that the QSVT can be efficiently "dequantized" for low-rank matrices, and discussed its implication to quantum machine learning. In this work, motivated by establishing the superiority of quantum algorithms for quantum chemistry and making progress on the quantum PCP conjecture, we focus on the other main class of matrices considered in applications of the QSVT, sparse matrices. We first show how to efficiently "dequantize", with arbitrarily small constant precision, the QSVT associated with a low-degree polynomial. We apply this technique to design classical algorithms that estimate, with constant precision, the singular values of a sparse matrix. We show in particular that a central computational problem considered by quantum algorithms for quantum chemistry (estimating the ground state energy of a local Hamiltonian when given, as an additional input, a state sufficiently close to the ground state) can be solved efficiently with constant precision on a classical computer. As a complementary result, we prove that with inverse-polynomial precision, the same problem becomes BQP-complete. This gives theoretical evidence for the superiority of quantum algorithms for chemistry, and strongly suggests that said superiority stems from the improved precision achievable in the quantum setting. We also discuss how this dequantization technique may help make progress on the central quantum PCP conjecture.}}, author = {{Gharibian, Sevag and Gall, François Le}}, booktitle = {{Proceedings of the 54th ACM Symposium on Theory of Computing (STOC)}}, pages = {{19--32}}, title = {{{Dequantizing the Quantum Singular Value Transformation: Hardness and Applications to Quantum Chemistry and the Quantum PCP Conjecture}}}, year = {{2022}}, } @inbook{46300, author = {{Niemann, Marco and Assenmacher, Dennis and Brunk, Jens and Riehle, Dennis Maximilian and Becker, Jörg and Trautmann, Heike}}, booktitle = {{Hate Speech — Definitionen, Ausprägungen, Lösungen}}, editor = {{Weitzel, Gerrit and Mündges, Stephan}}, isbn = {{978-3-658-35658-3}}, pages = {{249–274}}, publisher = {{VS Verlag für Sozialwissenschaften}}, title = {{{(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse}}}, doi = {{10.1007/978-3-658-35658-3_13}}, year = {{2022}}, } @inproceedings{46301, author = {{Assenmacher, D and Trautmann, Heike}}, booktitle = {{Intelligent Information and Database Systems}}, editor = {{et al. Tran, T}}, pages = {{3–16}}, publisher = {{Springer International Publishing}}, title = {{{Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption}}}, doi = {{10.1007/978-3-031-21743-2_1}}, year = {{2022}}, } @article{46316, abstract = {{ Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design. }}, author = {{Assenmacher, Dennis and Weber, Derek and Preuss, Mike and Valdez, André Calero and Bradshaw, Alison and Ross, Björn and Cresci, Stefano and Trautmann, Heike and Neumann, Frank and Grimme, Christian}}, journal = {{Social Science Computer Review}}, number = {{6}}, pages = {{1496--1522}}, title = {{{Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem}}}, doi = {{10.1177/08944393211012268}}, volume = {{40}}, year = {{2022}}, } @inproceedings{33957, abstract = {{Manufacturing companies are challenged to make the increasingly complex work processes equally manageable for all employees to prevent an impending loss of competence. In this contribution, an intelligent assistance system is proposed enabling employees to help themselves in the workplace and provide them with competence-related support. This results in increasing the short- and long-term efficiency of problem solving in companies.}}, author = {{Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt, Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}}, keywords = {{Assistance system, Knowledge graph, Information retrieval, Neural networks, AR}}, location = {{Stuttgart}}, title = {{{AI-Based Assistance System for Manufacturing}}}, doi = {{10.1109/ETFA52439.2022.9921520}}, year = {{2022}}, } @inproceedings{46306, abstract = {{Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.}}, author = {{Schneider, Lennart and Schäpermeier, Lennart and Prager, Raphael Patrick and Bischl, Bernd 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 = {{575–589}}, publisher = {{Springer International Publishing}}, title = {{{HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis}}}, doi = {{10.1007/978-3-031-14714-2_40}}, year = {{2022}}, } @article{46308, abstract = {{Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective optimization may have in the single-objective space. For this purpose, we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems based on first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective domain and subsequently exploiting local structures of the resulting landscapes. Our study particularly focuses on the sensitivity of this multiobjectivization approach w.r.t. (1) the parametrization of the artificial second objective, as well as (2) the position of the initial starting points in the search space. As SOMOGSA is a modular framework for encapsulating local search, we integrate Nelder–Mead local search as optimizer in the respective module and compare the performance of the resulting hybrid local search to its original single-objective counterpart. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Hence, combined with more sophisticated local search and metaheuristics, this may help solve highly multimodal optimization problems in the future.}}, author = {{Aspar, Pelin and Steinhoff, Vera and Schäpermeier, Lennart and Kerschke, Pascal and Trautmann, Heike and Grimme, Christian}}, journal = {{Natural Computing}}, pages = {{1–15}}, title = {{{The objective that freed me: a multi-objective local search approach for continuous single-objective optimization}}}, doi = {{10.1007/s11047-022-09919-w}}, volume = {{1}}, year = {{2022}}, } @inproceedings{48299, abstract = {{Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e.g., citation or social networks. However, sensitive personal information, such as documents with people{’}s profiles or relationships as edges, are prone to privacy leaks, as the trained model might reveal the original input. Although differential privacy (DP) offers a well-founded privacy-preserving framework, GCNs pose theoretical and practical challenges due to their training specifics. We address these challenges by adapting differentially-private gradient-based training to GCNs and conduct experiments using two optimizers on five NLP datasets in two languages. We propose a simple yet efficient method based on random graph splits that not only improves the baseline privacy bounds by a factor of 2.7 while retaining competitive F1 scores, but also provides strong privacy guarantees of epsilon = 1.0. We show that, under certain modeling choices, privacy-preserving GCNs perform up to 90{%} of their non-private variants, while formally guaranteeing strong privacy measures.}}, author = {{Igamberdiev, Timour and Habernal, Ivan}}, booktitle = {{Proceedings of the Thirteenth Language Resources and Evaluation Conference}}, pages = {{338–350}}, publisher = {{European Language Resources Association}}, title = {{{Privacy-Preserving Graph Convolutional Networks for Text Classification}}}, year = {{2022}}, } @inproceedings{48300, abstract = {{Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent.}}, author = {{Igamberdiev, Timour and Arnold, Thomas and Habernal, Ivan}}, booktitle = {{Proceedings of the 29th International Conference on Computational Linguistics}}, pages = {{2927–2933}}, publisher = {{International Committee on Computational Linguistics}}, title = {{{DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting}}}, year = {{2022}}, } @inproceedings{48298, author = {{Habernal, Ivan}}, booktitle = {{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}}, publisher = {{Association for Computational Linguistics}}, title = {{{How reparametrization trick broke differentially-private text representation learning}}}, doi = {{10.18653/v1/2022.acl-short.87}}, year = {{2022}}, } @inbook{49350, author = {{Brock, Jonathan and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Praxishandbuch Robotic Process Automation (RPA)}}, isbn = {{9783658383787}}, publisher = {{Springer Fachmedien Wiesbaden}}, title = {{{Nutzung von Process Mining in RPA-Projekten}}}, doi = {{10.1007/978-3-658-38379-4_5}}, year = {{2022}}, } @inproceedings{33983, author = {{Scholtysik, Michel and Rohde, Malte and Koldewey, Christian and Dumitrescu, Roman}}, title = {{{Adapting the product design to the circular economy using R-principles}}}, year = {{2022}}, } @inproceedings{30883, author = {{Krings, Sarah Claudia and Yigitbas, Enes and Biermeier, Kai and Engels, Gregor}}, booktitle = {{Proceedings of the 14th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2022)}}, title = {{{Design and Evaluation of AR-Assisted End-User Robot Path Planning Strategies}}}, year = {{2022}}, } @inproceedings{48861, abstract = {{Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by established approaches from the literature.}}, author = {{Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-9237-2}}, keywords = {{instance features, instance generation, quality diversity, TSP}}, pages = {{186–194}}, publisher = {{Association for Computing Machinery}}, title = {{{Exploring the Feature Space of TSP Instances Using Quality Diversity}}}, doi = {{10.1145/3512290.3528851}}, year = {{2022}}, } @inproceedings{48868, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-9268-6}}, pages = {{824–842}}, publisher = {{Association for Computing Machinery}}, title = {{{Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA}}}, doi = {{10.1145/3520304.3533626}}, year = {{2022}}, } @inproceedings{48882, abstract = {{In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.}}, author = {{Heins, Jonathan and Rook, Jeroen and Schäpermeier, Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}}, 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 Tusar, Tea}}, isbn = {{978-3-031-14714-2}}, keywords = {{Anytime behavior, Benchmarking, Continuous optimization, Multi-objective optimization, Multimodality, Performance metric}}, pages = {{192–206}}, publisher = {{Springer International Publishing}}, title = {{{BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}}}, doi = {{10.1007/978-3-031-14714-2_14}}, year = {{2022}}, } @inproceedings{48894, abstract = {{Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the literature.}}, author = {{Nikfarjam, Adel and Neumann, Aneta and Bossek, Jakob and Neumann, Frank}}, 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 Tu\v sar, Tea}}, isbn = {{978-3-031-14714-2}}, keywords = {{Co-evolutionary algorithms, Evolutionary diversity optimisation, Quality diversity, Traveling thief problem}}, pages = {{237–249}}, publisher = {{Springer International Publishing}}, title = {{{Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem}}}, doi = {{10.1007/978-3-031-14714-2_17}}, year = {{2022}}, }