@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}},
}

@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}},
}

@article{34197,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Comprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques.</jats:p>}},
  author       = {{Panzner, Melina and von Enzberg, Sebastian and Meyer, Maurice and Dumitrescu, Roman}},
  issn         = {{1868-7865}},
  journal      = {{Journal of the Knowledge Economy}},
  keywords     = {{Economics and Econometrics}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Characterization of Usage Data with the Help of Data Classifications}}},
  doi          = {{10.1007/s13132-022-01081-z}},
  year         = {{2022}},
}

@article{34196,
  abstract     = {{<jats:p>Mounting sensors in disk stack separators is often a major challenge due to the operating conditions. However, a process cannot be optimally monitored without sensors. Virtual sensors can be a solution to calculate the sought parameters from measurable values. We measured the vibrations of disk stack separators and applied machine learning (ML) to detect whether the separator contains only water or whether particles are also present. We combined seven ML classification algorithms with three feature engineering strategies and evaluated our model successfully on vibration data of an experimental disk stack separator. Our experimental results demonstrate that random forest in combination with manual feature engineering using domain specific knowledge about suitable features outperforms all other models with an accuracy of 91.27 %.</jats:p>}},
  author       = {{Merkelbach, Silke and Afroze, Lameya and Janssen, Nils and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}},
  issn         = {{2345-0533}},
  journal      = {{Vibroengineering PROCEDIA}},
  keywords     = {{General Medicine}},
  pages        = {{21--26}},
  publisher    = {{JVE International Ltd.}},
  title        = {{{Using vibration data to classify conditions in disk stack separators}}},
  doi          = {{10.21595/vp.2022.23000}},
  volume       = {{46}},
  year         = {{2022}},
}

@inbook{34195,
  author       = {{Hobscheidt, Daniela and Menzefricke, Jörn Steffen and Gabriel, Stefan and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{Praxishandbuch Robotic Process Automation (RPA)}},
  isbn         = {{9783658383787}},
  publisher    = {{Springer Fachmedien Wiesbaden}},
  title        = {{{Soziotechnische Herausforderungen bei der Einführung von RPA managen}}},
  doi          = {{10.1007/978-3-658-38379-4_8}},
  year         = {{2022}},
}

@inbook{34193,
  author       = {{Bansmann, Michael and Dumitrescu, Roman and Fechtelpeter, Christian}},
  booktitle    = {{Gestaltung digitalisierter Arbeitswelten}},
  isbn         = {{9783662580134}},
  issn         = {{2523-3637}},
  publisher    = {{Springer Berlin Heidelberg}},
  title        = {{{Transfer von Arbeit 4.0-Anwendungsszenarien}}},
  doi          = {{10.1007/978-3-662-58014-1_3}},
  year         = {{2022}},
}

@inbook{34194,
  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}},
}

@inbook{34292,
  author       = {{Wolters, Dennis and Engels, Gregor}},
  booktitle    = {{Product-Focused Software Process Improvement}},
  editor       = {{Taibi, Davide and Kuhrmann, Marco and Mikkonen, Tommi and Klünder, Jil and Abrahamsson, Pekka}},
  isbn         = {{9783031213878}},
  issn         = {{0302-9743}},
  pages        = {{235--242}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Towards Situational Process Management for Professional Education Programmes}}},
  doi          = {{10.1007/978-3-031-21388-5_16}},
  volume       = {{13709}},
  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}},
}

@article{27776,
  author       = {{Koldewey, Christian and Rasor, Anja and Reinhold, Jannik and Gausemeier, Jürgen and Dumitrescu, Roman and Chohan, Nadia and Frank, Maximilian}},
  issn         = {{0040-1625}},
  journal      = {{Technological Forecasting and Social Change}},
  publisher    = {{Elsevier}},
  title        = {{{Aligning strategic position, behavior, and structure for smart service businesses in manufacturing}}},
  doi          = {{10.1016/j.techfore.2021.121329}},
  year         = {{2022}},
}

@unpublished{29541,
  author       = {{Lienen, Christian and Platzner, Marco}},
  title        = {{{ReconROS Executor: Event-Driven Programming of FPGA-accelerated ROS 2 Applications}}},
  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}},
}

@inbook{32792,
  abstract     = {{Decision makers in complex business environments have different goals and constraints and therefore require tailored decision support systems (DSS). Following a low-code approach, a tailored DSS can be created by a decision maker as a process-based composition of existing, interoperable decision support services. Data incompatibilities may be introduced during the design or execution of such a process-driven DSS, e.g., when a service always generates or a decision maker selects data which violates a data constraint of a subsequent service. These incompatibilities cause interrupted or erroneous decision processes. In this paper, we contribute an approach which enables the detection of data incompatibilities in process-driven DSS during process design and execution. Our approach utilizes the JSON Schema specification to define service interfaces and associated type constraints which data produced by services or decision makers can be validated against. We demonstrate our approach in the context of decision support for energy network planning using a prototypical open-source implementation.}},
  author       = {{Kirchhoff, Jonas and Gottschalk, Sebastian and Engels, Gregor}},
  booktitle    = {{Lecture Notes in Business Information Processing}},
  isbn         = {{9783031115097}},
  issn         = {{1865-1348}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Detecting Data Incompatibilities in Process-Driven Decision Support Systems}}},
  doi          = {{10.1007/978-3-031-11510-3_6}},
  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}},
}