@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}},
}

@inproceedings{26539,
  abstract     = {{In control design most control strategies are model-based and require accurate models to be applied successfully. Due to simplifications and the model-reality-gap physics-derived models frequently exhibit deviations from real-world-systems. Likewise, purely data-driven methods often do not generalise well enough and may violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired loss functions separately have shown promising results to conquer these drawbacks. In this contribution we extend existing methods towards the identification of non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN and a physics-inspired loss term (-L) to successfully identify the system's dynamics, while maintaining the consistency with physical laws. The proposed method is demonstrated on two real-world nonlinear systems and outperforms existing techniques regarding complexity and reliability.}},
  author       = {{Götte, Ricarda-Samantha and Timmermann, Julia}},
  booktitle    = {{2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)}},
  keywords     = {{data-driven, physics-based, physics-informed, neural networks, system identification, hybrid modelling}},
  location     = {{Cairo, Egypt}},
  pages        = {{67--76}},
  title        = {{{Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering}}},
  doi          = {{10.1109/AIRC56195.2022.9836982}},
  year         = {{2022}},
}

@inproceedings{31066,
  abstract     = {{While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. }},
  author       = {{Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}},
  booktitle    = {{14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}},
  keywords     = {{neural networks, physics-guided, data-driven, multi-objective optimization, system identification, machine learning, dynamical systems}},
  location     = {{Casablanca, Morocco}},
  number       = {{12}},
  pages        = {{19--24}},
  title        = {{{Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}}},
  doi          = {{https://doi.org/10.1016/j.ifacol.2022.07.282}},
  volume       = {{55}},
  year         = {{2022}},
}

@article{37155,
  abstract     = {{Artificial intelligence (AI) has moved beyond the planning phase in many organisations and it is often accompanied by uncertainties and fears of job loss among employees. It is crucial to manage employees{\textquoteright} attitudes towards the deployment of an AI-based technology effectively and counteract possible resistance behaviour. We present lessons learned from an industry case where we conducted interviews with affected employees. We evaluated our results with managers across industries and found that that the deployment of AI-based technologies does not differ from other IT, but that the change is perceived differently due to misguided expectations. }},
  author       = {{Stieglitz, Stefan and Möllmann (Frick), Nicholas R. J. and Mirbabaie, Milad and Hofeditz, Lennart and Ross, Björn}},
  issn         = {{1477-9064}},
  journal      = {{International Journal of Management Practice}},
  keywords     = {{Artificial Intelligence, Change Management, Resistance, AI-Driven Change, AI Deployment, AI Perception}},
  publisher    = {{Inderscience}},
  title        = {{{Recommendations for Managing AI-Driven Change Processes: When Expectations Meet Reality}}},
  year         = {{2021}},
}

@inproceedings{27491,
  abstract     = {{ Students often have a lack of understanding and awareness of where, how, and why personal data about them is collected and processed. Especially, when interacting with data-driven digital artifacts, an appropriate perception of the data collection and processing is necessary for self-determination. This dissertation deals with the development and evaluation of a concept called data awareness which aims to foster students’ self-determination interacting with data-driven digital artifacts.}},
  author       = {{Höper, Lukas}},
  booktitle    = {{21st Koli Calling International Conference on Computing Education Research}},
  isbn         = {{9781450384889}},
  keywords     = {{data awareness, machine learning, data science education, data-driven digital artifacts, artificial intelligence}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Developing and Evaluating the Concept Data Awareness for K12 Computing Education}}},
  doi          = {{10.1145/3488042.3490509}},
  year         = {{2021}},
}

@inproceedings{16933,
  abstract     = {{The continuous innovation of its business models is an important task for a company to stay competitive. During this process, the company has to validate various hypotheses about its business models by adapting to uncertain and changing customer needs effectively and efficiently. This adaptation, in turn, can be supported by the concept of Software Product Lines (SPLs). SPLs reduce the time to market by deriving products for customers with changing requirements using a common set of features, structured as a feature model. Analogously, we support the process of business model adaptation by applying the engineering process of SPLs to the structure of the Business Model Canvas (BMC). We call this concept a Business Model Decision Line (BMDL). The BMDL matches business domain knowledge in the form of a feature model with customer needs to derive hypotheses about the business model together with experiments for validation. Our approach is effective by providing a comprehensive overview of possible business model adaptations and efficient by reusing experiments for different hypotheses. We implement our approach in a tool and illustrate the usefulness with an example of developing business models for a mobile application.}},
  author       = {{Gottschalk, Sebastian and Rittmeier, Florian and Engels, Gregor}},
  booktitle    = {{Proceedings of the 22nd IEEE International Conference on Business Informatics}},
  keywords     = {{Business Model Decision Line, Business Model Adaptation, Hypothesis-driven Adaptation, Software Product Line, Feature Model}},
  location     = {{Antwerp}},
  publisher    = {{IEEE}},
  title        = {{{Hypothesis-driven Adaptation of Business Models based on Product Line Engineering}}},
  doi          = {{10.1109/CBI49978.2020.00022}},
  year         = {{2020}},
}

@inproceedings{22737,
  author       = {{Becker, Matthias and Luckey, Markus and Becker, Steffen}},
  booktitle    = {{{Proceedings of the 8th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA)}}},
  isbn         = {{978-1-4503-1346-9}},
  keywords     = {{model-driven performance engineering, self-*, Self-adaptation, software performance}},
  pages        = {{117--122}},
  publisher    = {{ACM}},
  title        = {{{Model-driven Performance Engineering of Self-adaptive Systems: A Survey}}},
  doi          = {{10.1145/2304696.2304716}},
  year         = {{2012}},
}

@article{6092,
  abstract     = {{The topic of the present edition is visual masking paradigms-as powerful tool for demonstrating the processing of nonconscious visual information. In the present issue one article presents an improved methodology for disentangling perceptual and temporal influences in markers. Another paper demonstrates that preemptive control, or DPS, mediates the allocation of attention towards possible targets. One of the contributions specify conditions under which DPS-like effects are found as opposed to conditions under which stimulus-driven effects are found. A study of two illusions which the prime may cause in a trailing stimulus, a temporal pre-dating of the mask and a perception of motion in later stimuli adjacent to the prime is presented in the issue. Another contribution addresses how the percept of a stimulus is altered by a temporal and spatial interplay of two backward masks or of one forward mask and two backwards masks. (PsycINFO Database Record (c) 2016 APA, all rights reserved)}},
  author       = {{Scharlau, Ingrid and Ansorge, Ulrich and Breitmeyer, Bruno G.}},
  issn         = {{1895-1171}},
  journal      = {{Advances in Cognitive Psychology}},
  keywords     = {{visual masking, visual information, attention, stimulus-driven effects, motion perception, Attention, Illusions (Perception), Motion Perception, Visual Masking}},
  number       = {{1}},
  pages        = {{1 -- 5}},
  title        = {{{Trends and styles in visual masking.}}},
  volume       = {{2}},
  year         = {{2006}},
}