@inproceedings{31806,
  abstract     = {{The creation of an RDF knowledge graph for a particular application commonly involves a pipeline of tools that transform a set ofinput data sources into an RDF knowledge graph in a process called dataset augmentation. The components of such augmentation pipelines often require extensive configuration to lead to satisfactory results. Thus, non-experts are often unable to use them. Wepresent an efficient supervised algorithm based on genetic programming for learning knowledge graph augmentation pipelines of arbitrary length. Our approach uses multi-expression learning to learn augmentation pipelines able to achieve a high F-measure on the training data. Our evaluation suggests that our approach can efficiently learn a larger class of RDF dataset augmentation tasks than the state of the art while using only a single training example. Even on the most complex augmentation problem we posed, our approach consistently achieves an average F1-measure of 99% in under 500 iterations with an average runtime of 16 seconds}},
  author       = {{Dreßler, Kevin and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia}},
  keywords     = {{2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba}},
  location     = {{Barcelona (Spain)}},
  title        = {{{ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning}}},
  doi          = {{10.1145/3511095.3531287}},
  year         = {{2022}},
}

@article{32854,
  author       = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}},
  journal      = {{IFAC-PapersOnLine}},
  number       = {{13}},
  pages        = {{133–138}},
  publisher    = {{Elsevier}},
  title        = {{{Practical Network Conditions for the Convergence of Distributed Optimization}}},
  volume       = {{55}},
  year         = {{2022}},
}

@inproceedings{33253,
  author       = {{Hansmeier, Tim and Brede, Mathis and Platzner, Marco}},
  booktitle    = {{GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  location     = {{Boston, MA, USA}},
  pages        = {{2071--2079}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{{XCS on Embedded Systems: An Analysis of Execution Profiles and Accelerated Classifier Deletion}}},
  doi          = {{10.1145/3520304.3533977}},
  year         = {{2022}},
}

@inproceedings{33274,
  author       = {{Chen, Wei-Fan and Chen, Mei-Hua and Mudgal, Garima and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)}},
  pages        = {{51 -- 61}},
  title        = {{{Analyzing Culture-Specific Argument Structures in Learner Essays}}},
  year         = {{2022}},
}

@inproceedings{33491,
  author       = {{Maack, Marten and Pukrop, Simon and Rasmussen, Anna Rodriguez}},
  booktitle    = {{30th Annual European Symposium on Algorithms, ESA 2022, September 5-9, 2022, Berlin/Potsdam, Germany}},
  editor       = {{Chechik, Shiri and Navarro, Gonzalo and Rotenberg, Eva and Herman, Grzegorz}},
  pages        = {{77:1–77:13}},
  publisher    = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}},
  title        = {{{(In-)Approximability Results for Interval, Resource Restricted, and Low Rank Scheduling}}},
  doi          = {{10.4230/LIPIcs.ESA.2022.77}},
  volume       = {{244}},
  year         = {{2022}},
}

@techreport{32106,
  abstract     = {{We study the consequences of modeling asymmetric bargaining power in two-person bargaining problems. Comparing application of an asymmetric version of a bargaining solution to an upfront modification of the disagreement point, the resulting distortion crucially depends on the bargaining solution concept. While for the Kalai-Smorodinsky solution weaker players benefit from modifying the disagreement point, the situation is reversed for the Nash bargaining solution. There, weaker players are better off in the asymmetric bargaining solution. When comparing application of the asymmetric versions of the Nash and the Kalai-Smorodinsky solutions, we demonstrate that there is an upper bound for the weight of a player, so that she is better off with the Nash bargaining solution. This threshold is ultimately determined by the relative utilitarian bargaining solution. From a mechanism design perspective, our results provide valuable information for a social planner, when implementing a bargaining solution for unequally powerful players.}},
  author       = {{Haake, Claus-Jochen and Streck, Thomas}},
  keywords     = {{Asymmetric bargaining power, Nash bargaining solution, Kalai-Smorodinsky bargaining solution}},
  pages        = {{17}},
  title        = {{{Distortion through modeling asymmetric bargaining power}}},
  volume       = {{148}},
  year         = {{2022}},
}

@inbook{32179,
  abstract     = {{This work addresses the automatic resolution of software requirements. In the vision of On-The-Fly Computing, software services should be composed on demand, based solely on natural language input from human users. To enable this, we build a chatbot solution that works with human-in-the-loop support to receive, analyze, correct, and complete their software requirements. The chatbot is equipped with a natural language processing pipeline and a large knowledge base, as well as sophisticated dialogue management skills to enhance the user experience. Previous solutions have focused on analyzing software requirements to point out errors such as vagueness, ambiguity, or incompleteness. Our work shows how apps can collaborate with users to efficiently produce correct requirements. We developed and compared three different chatbot apps that can work with built-in knowledge. We rely on ChatterBot, DialoGPT and Rasa for this purpose. While DialoGPT provides its own knowledge base, Rasa is the best system to combine the text mining and knowledge solutions at our disposal. The evaluation shows that users accept 73% of the suggested answers from Rasa, while they accept only 63% from DialoGPT or even 36% from ChatterBot.}},
  author       = {{Kersting, Joschka and Ahmed, Mobeen and Geierhos, Michaela}},
  booktitle    = {{HCI International 2022 Posters}},
  editor       = {{Stephanidis, Constantine and Antona, Margherita and Ntoa, Stavroula}},
  isbn         = {{9783031064166}},
  issn         = {{1865-0929}},
  keywords     = {{On-The-Fly Computing, Chatbot, Knowledge Base}},
  location     = {{Virtual}},
  pages        = {{419----426}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Chatbot-Enhanced Requirements Resolution for Automated Service Compositions}}},
  doi          = {{10.1007/978-3-031-06417-3_56}},
  volume       = {{1580}},
  year         = {{2022}},
}

@phdthesis{29769,
  abstract     = {{Wettstreit zwischen der Entwicklung neuer Hardwaretrojaner und entsprechender Gegenmaßnahmen beschreiten Widersacher immer raffiniertere Wege um Schaltungsentwürfe zu infizieren und dabei selbst fortgeschrittene Test- und Verifikationsmethoden zu überlisten. Abgesehen von den konventionellen Methoden um einen Trojaner in eine Schaltung für ein Field-programmable Gate Array (FPGA) einzuschleusen, können auch die Entwurfswerkzeuge heimlich kompromittiert werden um einen Angreifer dabei zu unterstützen einen erfolgreichen Angriff durchzuführen, der zum Beispiel Fehlfunktionen oder ungewollte Informationsabflüsse bewirken kann. Diese Dissertation beschäftigt sich hauptsächlich mit den beiden Blickwinkeln auf Hardwaretrojaner in rekonfigurierbaren Systemen, einerseits der Perspektive des Verteidigers mit einer Methode zur Erkennung von Trojanern auf der Bitstromebene, und andererseits derjenigen des Angreifers mit einer neuartigen Angriffsmethode für FPGA Trojaner. Für die Verteidigung gegen den Trojaner ``Heimtückische LUT'' stellen wir die allererste erfolgreiche Gegenmaßnahme vor, die durch Verifikation mittels Proof-carrying Hardware (PCH) auf der Bitstromebene direkt vor der Konfiguration der Hardware angewendet werden kann, und präsentieren ein vollständiges Schema für den Entwurf und die Verifikation von Schaltungen für iCE40 FPGAs. Für die Gegenseite führen wir einen neuen Angriff ein, welcher bösartiges Routing im eingefügten Trojaner ausnutzt um selbst im fertigen Bitstrom in einem inaktiven Zustand zu verbleiben: Hierdurch kann dieser neuartige Angriff zur Zeit weder von herkömmlichen Test- und Verifikationsmethoden, noch von unserer vorher vorgestellten Verifikation auf der Bitstromebene entdeckt werden.}},
  author       = {{Ahmed, Qazi Arbab}},
  keywords     = {{FPGA Security, Hardware Trojans, Bitstream-level Trojans, Bitstream Verification}},
  publisher    = {{ Paderborn University, Paderborn, Germany}},
  title        = {{{Hardware Trojans in Reconfigurable Computing}}},
  doi          = {{10.17619/UNIPB/1-1271}},
  year         = {{2022}},
}

@inproceedings{31054,
  abstract     = {{This paper aims at discussing past limitations set in sentiment analysis research regarding explicit and implicit mentions of opinions. Previous studies have regularly neglected this question in favor of methodical research on standard-datasets. Furthermore, they were limited to linguistically less-diverse domains, such as commercial product reviews. We face this issue by annotating a German-language physician review dataset that contains numerous implicit, long, and complex statements that indicate aspect ratings, such as the physician’s friendliness. We discuss the nature of implicit statements and present various samples to illustrate the challenge described.}},
  author       = {{Kersting, Joschka and Bäumer, Frederik Simon}},
  booktitle    = {{Proceedings of the Fourteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence - Data Science for Real-World Applications}},
  editor       = {{Kersting, Joschka}},
  keywords     = {{Sentiment analysis, Natural language processing, Aspect phrase extraction}},
  location     = {{Barcelona, Spain}},
  pages        = {{5--9}},
  publisher    = {{IARIA}},
  title        = {{{Implicit Statements in Healthcare Reviews: A Challenge for Sentiment Analysis}}},
  year         = {{2022}},
}

@article{31881,
  author       = {{Hoyer, Britta and De Jaegher, Kris}},
  journal      = {{International Journal of Game Theory}},
  publisher    = {{Springer}},
  title        = {{{Network Disruption and the Common-Enemy Effect}}},
  doi          = {{10.1007/s00182-022-00812-5}},
  year         = {{2022}},
}

@article{33250,
  author       = {{Szopinski, Daniel and Massa, Lorenzo and John, Thomas and Kundisch, Dennis and Tucci, Christopher}},
  journal      = {{Communications of the Association for Information Systems}},
  pages        = {{774--841}},
  title        = {{{Modeling Business Models: A cross-disciplinary Analysis of Business Model Modeling Languages and Directions for Future Research}}},
  volume       = {{51}},
  year         = {{2022}},
}

@inproceedings{29220,
  abstract     = {{Modern services often comprise several components, such as chained virtual network functions, microservices, or
machine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. 
To overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. 

Instead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios.}},
  author       = {{Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}},
  booktitle    = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}},
  keywords     = {{network management, service management, AI, Monte Carlo Tree Search, model-based, QoS}},
  location     = {{Budapest}},
  publisher    = {{IEEE}},
  title        = {{{Use What You Know: Network and Service Coordination Beyond Certainty}}},
  year         = {{2022}},
}

@phdthesis{29763,
  abstract     = {{Modern-day communication has become more and more digital. While this comes with many advantages such as a more efficient economy, it has also created more and more opportunities for various adversaries to manipulate communication or eavesdrop on it. The Snowden revelations in 2013 further highlighted the seriousness of these threats. To protect the communication of people, companies, and states from such threats, we require cryptography with strong security guarantees.
Different applications may require different security properties from cryptographic schemes. For most applications, however, so-called adaptive security is considered a reasonable minimal requirement of security. Cryptographic schemes with adaptive security remain secure in the presence of an adversary that can corrupt communication partners to respond to messages of the adversaries choice, while the adversary may choose the messages based on previously observed interactions.
While cryptography is associated the most with encryption, this is only one of many primitives that are essential for the security of digital interactions. This thesis presents novel identity-based encryption (IBE) schemes and verifiable random functions (VRFs) that achieve adaptive security as outlined above. Moreover, the cryptographic schemes presented in this thesis are proven secure in the standard model. That is without making use of idealized models like the random oracle model.}},
  author       = {{Niehues, David}},
  keywords     = {{public-key cryptography, lattices, pairings, verifiable random functions, identity-based encryption}},
  title        = {{{More Efficient Techniques for Adaptively-Secure Cryptography}}},
  doi          = {{10.25926/rdtq-jw45}},
  year         = {{2022}},
}

@inproceedings{31068,
  author       = {{Chen, Mei-Hua and Mudgal, Garima and Chen, Wei-Fan and Wachsmuth, Henning}},
  booktitle    = {{EUROCALL}},
  title        = {{{Investigating the argumentation structures of EFL learners from diverse language backgrounds}}},
  year         = {{2022}},
}

@article{31479,
  author       = {{Baswana, Surender and Gupta, Shiv and Knollmann, Till}},
  issn         = {{0178-4617}},
  journal      = {{Algorithmica}},
  keywords     = {{Applied Mathematics, Computer Science Applications, General Computer Science}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Mincut Sensitivity Data Structures for the Insertion of an Edge}}},
  doi          = {{10.1007/s00453-022-00978-0}},
  year         = {{2022}},
}

@misc{33033,
  author       = {{Fehring, Lukas}},
  title        = {{{Combined Ranking and Regression Trees for Algorithm Selection}}},
  year         = {{2022}},
}

@unpublished{30867,
  abstract     = {{In online algorithm selection (OAS), instances of an algorithmic problem
class are presented to an agent one after another, and the agent has to quickly
select a presumably best algorithm from a fixed set of candidate algorithms.
For decision problems such as satisfiability (SAT), quality typically refers to
the algorithm's runtime. As the latter is known to exhibit a heavy-tail
distribution, an algorithm is normally stopped when exceeding a predefined
upper time limit. As a consequence, machine learning methods used to optimize
an algorithm selection strategy in a data-driven manner need to deal with
right-censored samples, a problem that has received little attention in the
literature so far. In this work, we revisit multi-armed bandit algorithms for
OAS and discuss their capability of dealing with the problem. Moreover, we
adapt them towards runtime-oriented losses, allowing for partially censored
data while keeping a space- and time-complexity independent of the time
horizon. In an extensive experimental evaluation on an adapted version of the
ASlib benchmark, we demonstrate that theoretically well-founded methods based
on Thompson sampling perform specifically strong and improve in comparison to
existing methods.}},
  author       = {{Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings of the 36th AAAI Conference on Artificial Intelligence}},
  publisher    = {{AAAI}},
  title        = {{{Machine Learning for Online Algorithm Selection under Censored Feedback}}},
  year         = {{2022}},
}

@unpublished{30865,
  abstract     = {{The problem of selecting an algorithm that appears most suitable for a
specific instance of an algorithmic problem class, such as the Boolean
satisfiability problem, is called instance-specific algorithm selection. Over
the past decade, the problem has received considerable attention, resulting in
a number of different methods for algorithm selection. Although most of these
methods are based on machine learning, surprisingly little work has been done
on meta learning, that is, on taking advantage of the complementarity of
existing algorithm selection methods in order to combine them into a single
superior algorithm selector. In this paper, we introduce the problem of meta
algorithm selection, which essentially asks for the best way to combine a given
set of algorithm selectors. We present a general methodological framework for
meta algorithm selection as well as several concrete learning methods as
instantiations of this framework, essentially combining ideas of meta learning
and ensemble learning. In an extensive experimental evaluation, we demonstrate
that ensembles of algorithm selectors can significantly outperform single
algorithm selectors and have the potential to form the new state of the art in
algorithm selection.}},
  author       = {{Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  booktitle    = {{Machine Learning}},
  title        = {{{Algorithm Selection on a Meta Level}}},
  year         = {{2022}},
}

@article{33090,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Heated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially depends on a suitable choice of the parameters of the welding process, such as heating time, temperature, and the precise way how the parts are then welded. Moreover, when different materials are to be joined, the parameter values need to be tailored to the specifics of the respective material. To this end, in this paper, three approaches to tailor the parameter values to optimize the quality of the connection are compared: a heuristic by Potente, statistical experimental design, and Bayesian optimization. With the suitability for practice in mind, a series of experiments are carried out with these approaches, and their capabilities of proposing well-performing parameter values are investigated. As a result, Bayesian optimization is found to yield peak performance, but the costs for optimization are substantial. In contrast, the Potente heuristic does not require any experimentation and recommends parameter values with competitive quality.</jats:p>}},
  author       = {{Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik and Schöppner, Volker and Hüllermeier, Eyke}},
  issn         = {{0043-2288}},
  journal      = {{Welding in the World}},
  keywords     = {{Metals and Alloys, Mechanical Engineering, Mechanics of Materials}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials}}},
  doi          = {{10.1007/s40194-022-01339-9}},
  year         = {{2022}},
}

@inproceedings{33230,
  author       = {{Daymude, Joshua J. and Richa, Andréa W. and Scheideler, Christian}},
  booktitle    = {{1st Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2022, March 28-30, 2022, Virtual Conference}},
  editor       = {{Aspnes, James and Michail, Othon}},
  pages        = {{12:1–12:19}},
  publisher    = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}},
  title        = {{{Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems}}},
  doi          = {{10.4230/LIPIcs.SAND.2022.12}},
  volume       = {{221}},
  year         = {{2022}},
}