Neural Class Expression Synthesis in ALCHIQ(D) - Kouagou, N'Dah Jean; Heindorf, Stefan; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (Machine Learning and Knowledge Discovery in Databases: Research Track)
Class expression learning in description logics has long been regarded as an iterative search problem in an infinite conceptual space. Each iteration of the search process invokes a reasoner and a heuristic function. The reasoner finds the instances of the current expression, and the heuristic function computes the information gain and decides on the next step to be taken. As the size of the background knowledge base grows, search-based approaches for class expression learning become prohibitively slow. Current neural class expression synthesis (NCES) approaches investigate the use of neural networks for class expression learning in the attributive language with complement (ALC). While they show significant improvements over search-based approaches in runtime and quality of the computed solutions, they rely on the availability of pretrained embeddings for the input knowledge base. Moreover, they are not applicable to ontologies in more expressive description logics. In this paper, we propose a novel NCES approach which extends the state of the art to the description logic ALCHIQ(D). Our extension, dubbed NCES2, comes with an improved training data generator and does not require pretrained embeddings for the input knowledge base as both the embedding model and the class expression synthesizer are trained jointly. Empirical results on benchmark datasets suggest that our approach inherits the scalability capability of current NCES instances with the additional advantage that it supports more complex learning problems. NCES2 achieves the highest performance overall when compared to search-based approaches and to its predecessor NCES. We provide our source code, datasets, and pretrained models at https://github.com/dice-group/NCES2.

Extremwettersituationen in alpinen Gebieten: Management kritischer Situationen in Echtzeit bei extremen und komplexen Daten - Pottebaum, Jens; Rechberger, Christina; Hieb, Michael; Gräßler, Iris; Resch, Christian (Tagungsband der Fachtagung Katastrophenforschung 2023)
Extremwettersituationen sind durch die Kombination von globalen und lokalen Wirkzusammenhän-gen gekennzeichnet. In der Gefahrenanalyse und -reaktion ist deshalb der Umgang mit extremen Daten erforderlich, die von heterogenen Datenquellen bezogen und mittels unterschiedlicher Ver-fahren bis hin zum maschinellen Lernen ausgewertet werden. Die Visualisierung dieser zwangsläufig unsicherheitsbehafteten Daten stellt eine Herausforderung dar. Diese wirkt umso bedeutsamer, je weniger Fachexpertise in Bereichen wie Meteorologie, Geologie oder Sensortechnik in einer Füh-rungs- oder Leitstelle eingebunden werden kann. Das Management kritischer Situationen in Echtzeit bei extremen und komplexen Daten muss daher auf einer Bewertung der Informationsqualität von extremen Daten beruhen. Diese Bewertung ist abhängig vom Anwendungskontext in unterschiedli-chen Führungs- und Assistenzstellen sowie der verfügbaren Infrastruktur mit Geräten zur Visualisie-rung, Schnittstellen von Wetterdiensten, Sensorsysteme und Rettungsrobotik. Im Beitrag wird der Ansatz des EU-Projekts CREXDATA in Bezug auf mögliche pluviale Hochwassersituationen in Inns-bruck vorgestellt. Grundlage bildet die Kategorisierung von extremen Daten, die Schnittstellen zu Datenquellen mit globalem und lokalem Bezug sowie Anwendungsfälle für die Visualisierung von Informationen. Es werden somit Grundlagen präsentiert, die in allen Formen von geobasierten Lage- und Führungsinformationssystemen zum Einsatz kommen können.

Digital Transformation - Towards flexible human-centric enterprises - Kehrbusch, Burkhard; Engels, Gregor (Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective)

Optimization of optical waveguide antennas for directive emission of light - Farheen, Henna; Leuteritz, Till; Linden, Stefan; Myroshnychenko, Viktor; Förstner, Jens (Journal of the Optical Society of America B)
Optical traveling wave antennas offer unique opportunities to control and selectively guide light into a specific direction, which renders them excellent candidates for optical communication and sensing. These applications require state-of-the-art engineering to reach optimized functionalities such as high directivity and radiation efficiency, low sidelobe levels, broadband and tunable capabilities, and compact design. In this work, we report on the numerical optimization of the directivity of optical traveling wave antennas made from low-loss dielectric materials using full-wave numerical simulations in conjunction with the particle swarm optimization algorithm. The antennas are composed of a reflector and a director deposited on a glass substrate, and an emitter placed in the feed gap between them serves as an internal source of excitation. In particular, we analyze antennas with rectangular- and horn-shaped directors made of either hafnium dioxide or silicon. The optimized antennas produce highly directional emissions due to the presence of two dominant guided TE modes in the director in addition to leaky modes. These guided modes dominate the far-field emission pattern and govern the direction of the main lobe emission, which predominately originates from the end facet of the director. Our work also provides a comprehensive analysis of the modes, radiation patterns, parametric influences, and bandwidths of the antennas, which highlights their robust nature.
Keywords: tet_topic_opticalantenna

Utilizing Geographic Information Systems for Condition-Based Maintenance on the Energy Distribution Grid - zur Heiden, Philipp; Priefer, Jennifer; Beverungen, Daniel (Proceedings of the 55th Hawaii International Conference on System Sciences)

Context in Design Science Research: Taxonomy and Framework - Herwix, Alexander; zur Heiden, Philipp (Proceedings of the 55th Hawaii International Conference on System Sciences)

A Renaissance of Context in Design Science Research - zur Heiden, Philipp; Beverungen, Daniel (Proceedings of the 55th Hawaii International Conference on System Sciences)

Introduction to the Data-driven Services in Manufacturing Minitrack - Exploring Management, Engineering, and Organizational Transformation - Koldewey, Christian; Dumitrescu, Roman; Rabe, Martin (Proceedings of the 55th Hawaii International Conference on System Sciences)

Visual Interpretability of Image-based Real Estate Appraisal - Kucklick, Jan-Peter (55th Annual Hawaii International Conference on System Sciences (HICSS-55))
Explainability for machine learning gets more and more important in high-stakes decisions like real estate appraisal. While traditional hedonic house pricing models are fed with hard information based on housing attributes, recently also soft information has been incorporated to increase the predictive performance. This soft information can be extracted from image data by complex models like Convolutional Neural Networks (CNNs). However, these are intransparent which excludes their use for high-stakes financial decisions. To overcome this limitation, we examine if a two-stage modeling approach can provide explainability. We combine visual interpretability by Regression Activation Maps (RAM) for the CNN and a linear regression for the overall prediction. Our experiments are based on 62.000 family homes in Philadelphia and the results indicate that the CNN learns aspects related to vegetation and quality aspects of the house from exterior images, improving the predictive accuracy of real estate appraisal by up to 5.4%.
Keywords: Explainable Artificial Intelligence (XAI); Regression Activation Maps; Real Estate Appraisal; Convolutional Block Attention Module; Computer Vision

Using Geolocated Text to Quantify Location in Real Estate Appraisal - Heuwinkel, Tim; Kucklick, Jan-Peter; Müller, Oliver (55th Annual Hawaii International Conference on System Sciences (HICSS-55))
Accurate real estate appraisal is essential in decision making processes of financial institutions, governments, and trending real estate platforms like Zillow. One of the most important factors of a property’s value is its location. However, creating accurate quantifications of location remains a challenge. While traditional approaches rely on Geographical Information Systems (GIS), recently unstructured data in form of images was incorporated in the appraisal process, but text data remains an untapped reservoir. Our study shows that using text data in form of geolocated Wikipedia articles can increase predictive performance over traditional GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to automatically extract geographically weighted vector representations for text is established and used alongside traditional structural housing features to make predictions and to uncover local patterns on sale price for real estate transactions between 2015 and 2020 in Allegheny County, Pennsylvania.
Keywords: Real Estate Appraisal; Text Regression; Natural Language Processing (NLP); Location Intelligence; Wikipedia

Capacity investments in a competitive energy market - Block, Lukas; Westbrock, Bastian
We study the abilities of competitive markets to produce sufficient energy capacities to meet a fixed energy demand. Renewable energy producers with stochastic outputs and no variable costs compete against conventional energy producers with deterministic, pollutant outputs and increasing marginal costs. We find that either market forces are strong enough to serve the entire demand, or they are too weak such that the market fails and nothing is produced. This crucially depends on the relative cost of renewable energy investments, such that relatively cheap renewable energy causes the market to fail. Welfare analyses show that with increasing levels of conventional energy pollution the ability of the market to produce an ecient outcome further declines. As a policy implication, our findings refute the use of a strategic reserve as a blackout backstop solution. Instead, a capacity mechanism consisting of a tax-and-subsidy scheme can align the market outcome with the eficient solution for all pollution levels and relative costs of renewable energy capacities.
Keywords: Renewable versus conventional energy; capacity mechanisms; strategic reserves; capacity payments

Situational Business Model Developer: A Tool-support for Situation-speciﬁc Business Model Development - Gottschalk, Sebastian; Yigitbas, Enes; Nowosad, Alexander; Engels, Gregor (Proceedings of the 17th International Conference on Wirtschaftsinformatik)
The development of business models is a challenging task that can be supported with software tools. Here, existing approaches and tools do not focus on the company’s situation in which the development takes place (e.g., ﬁnancial resources, product type). To tackle this challenge, we used design science research to develop a situation-speciﬁc business model development approach that contains three stages: First, existing knowledge in terms of tasks to do (e.g., analyze competitive advantage), and decisions to be made (e.g., social media marketing) are stored in repositories. Second, the knowledge is used to compose a development method based on the company’s situation. Third, the development method is enacted to develop a business model. This demonstration paper presents a tool-support called Situational Business Model Developer that supports all stages of our approach. We release the tool under open-source and evaluate it with a case study on developing business models for mobile apps.
Keywords: Business Model Development; Situational Method Engineering; Tool Support

Development and Evaluation of a Collaborative Stock Trading Environment in Virtual Reality - Yigitbas, Enes; Gottschalk, Sebastian; Nowosad, Alexander; Engels, Gregor (Proceedings of the 17th International Conference on Wirtschaftsinformatik)
Due to the proliferation of Virtual Reality (VR) technology, VR is finding new applications in various domains, such as stock trading. Here, traders invest in stocks intending to increase their profit. For this purpose, in conventional stock trading, traders usually make use of 2D applications on desktop or laptop devices. This leads to many drawbacks such as poor visibility due to limited 2D representation, complex interaction due to indirect interaction via mouse and keyboard, or restricted support for collaboration between traders. To overcome these issues, we have developed a novel collaborative, virtual environment for stock trading, which enables stock traders to view financial information and trade stocks with other collaborators. The main results of a usability study indicate that the VR environment, compared to conventional stock trading, shows no significant advantages concerning efficiency and effectiveness, however, we could observe an increased user satisfaction and better collaboration.
Keywords: virtual reality; stock trading; collaboration; usability

Funnel control of linear systems under output measurement losses - Lanza, Lukas Johannes; Berger, Thomas

A Versatile Metasurface Enabling Superwettability for Self‐Cleaning and Dynamic Color Response - Lu, Jinlong; Sain, Basudeb; Georgi, Philip; Protte, Maximilian; Bartley, Tim; Zentgraf, Thomas (Advanced Optical Materials)
Metasurfaces provide applications for a variety of flat elements and devices due to the ability to modulate light with subwavelength structures. The working principle meanwhile gives rise to the crucial problem and challenge to protect the metasurface from dust or clean the unavoidable contaminants during daily usage. Here, taking advantage of the intelligent bioinspired surfaces which exhibit self-cleaning properties, a versatile dielectric metasurface benefiting from the obtained superhydrophilic or quasi-superhydrophobic states is shown. The design is realized by embedding the metasurface inside a large area of wettability supporting structures, which is highly efficient in fabrication, and achieves both optical and wettability functionality at the same time. The superhydrophilic state enables an enhanced optical response with water, while the quasi-superhydrophobic state imparts the fragile antennas an ability to self-clean dust contamination. Furthermore, the metasurface can be easily switched and repeated between these two wettability or functional states by appropriate treatments in a repeatable way, without degrading the optical performance. The proposed design strategy will bring new opportunities to smart metasurfaces with improved optical performance, versatility, and physical stability.

mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks - Schneider, Stefan Balthasar; Werner, Stefan; Khalili, Ramin; Hecker, Artur; Karl, Holger (IEEE/IFIP Network Operations and Management Symposium (NOMS))
Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive results. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field. To this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of wireless mobile networks.
Keywords: wireless mobile networks; network management; continuous control; cognitive networks; autonomous coordination; reinforcement learning; gym environment; simulation; open source

ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation - Banholzer, Stefan; Gebken, Bennet; Dellnitz, Michael; Peitz, Sebastian; Volkwein, Stefan (Non-Smooth and Complementarity-Based Distributed Parameter Systems)
Multiobjective optimization plays an increasingly important role in modern applications, where several objectives are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging which is particularly problematic when the objectives are costly to evaluate as is the case for models governed by partial differential equations (PDEs). To decrease the numerical effort to an affordable amount, surrogate models can be used to replace the expensive PDE evaluations. Existing multiobjective optimization methods using model reduction are limited either to low parameter dimensions or to few (ideally two) objectives. In this article, we present a combination of the reduced basis model reduction method with a continuation approach using inexact gradients. The resulting approach can handle an arbitrary number of objectives while yielding a significant reduction in computing time.

Second Harmonic Optical Circular Dichroism of Plasmonic Chiral Helicoid-III Nanoparticles - Spreyer, Florian; Mun, Jungho; Kim, Hyeohn; Kim, Ryeong Myeong; Nam, Ki Tae; Rho, Junsuk; Zentgraf, Thomas (ACS Photonics)
While plasmonic particles can provide optical resonances in a wide spectral range from the lower visible up to the near-infrared, often, symmetry effects are utilized to obtain particular optical responses. By breaking certain spatial symmetries, chiral structures arise and provide robust chiroptical responses to these plasmonic resonances. Here, we observe strong chiroptical responses in the linear and nonlinear optical regime for chiral L-handed helicoid-III nanoparticles and quantify them by means of an asymmetric factor, the so-called g-factor. We calculate the linear optical g-factors for two distinct chiroptical resonances to −0.12 and –0.43 and the nonlinear optical g-factors to −1.45 and −1.63. The results demonstrate that the chirality of the helicoid-III nanoparticles is strongly enhanced in the nonlinear regime.
Keywords: Electrical and Electronic Engineering; Atomic and Molecular Physics; and Optics; Biotechnology; Electronic; Optical and Magnetic Materials

Resonant evanescent excitation of OAM modes in a high-contrast circular step-index fiber - Hammer, Manfred; Ebers, Lena; Förstner, Jens (Complex Light and Optical Forces XVI)
Resonant evanescent coupling can be utilized to selectively excite orbital angular momentum (OAM) modes of high angular order supported by a thin circular dielectric rod. Our 2.5-D hybrid-analytical coupled mode model combines the vectorial fields associated with the fundamental TE- and TM-modes of a standard silicon photonics slab waveguide, propagating at oblique angles with respect to the rod axis, and the hybrid modes supported by the rod. One observes an efficient resonant interaction in cases where the common axial wavenumber of the waves in the slab matches the propagation constant of one or more modes of the rod. For certain modes of high angular order, the incident wave is able to transfer its directionality to the field in the fiber, exciting effectively only one of a pair of degenerate OAM modes
Keywords: tet_topic_waveguide

DASC-PM v1.1 - Ein Vorgehensmodell für Data-Science-Projekte - Schulz, Michael; Neuhaus, Uwe; Kaufmann, Jens; Kühnel, Stephan; Alekozai, Emal M.; Rohde, Heiko; Hoseini, Sayed; Theuerkauf, René; Badura, Daniel; Kerzel, Ulrich; Lanquillon, Carsten; Daurer, Stephan; et al.