Data4UrbanMobility hat auf der renommierten SIGSPATIAL-Konferenz 2019 (International Conference on Advances in Geographic Information Systems), das Poster “ST-Discovery: Data-Driven Discovery of Structural Dependencies in Urban Road Networks” vorgestellt. Das Poster-Paper beschäftigt sich damit, Abhängigkeiten in Straßennetzwerken zu identifizieren, die auf Grund der Topologie des Straßennetzwerkes entstehen. Das volle Poster-Paper finden Sie hier: ST-Discovery: Data-Driven Discovery of Structural Dependencies in Urban Road Networks. Tempelmeier, Nicolas; Wage, Oskar; Feuerhake, Udo; Demidova, Elena. (2019) URL: https://dl.acm.org/citation.cfm?id=3359109

• Planlos ins sozial-ökologische Desaster: Das Versagen der europäischen Autoindustrie. Boewe, Jörn; Schulten, Johannes (2024). (9) 101–107.
• One Size Fits All? The Significance of Digital Nursing Documentation for Communication with Patients. Bringmann, Julia; Petersen, Benjamin Henry; Staab, Philipp (2024). 39–44.
• Stability of Weyl node merging processes under symmetry constraints. Naselli, Gabriele; Frank, György; Varjas, Dániel; Fulga, Ion Cosma; Pintér, Gergő; Pályi, András; Könye, Viktor (2024). 133(19) 196602.
• Spin orbital lattice entanglement in the ideal j=\($\mathbf\dfrac12$\) compound K\($_\mathbf2$\)IrCl\($_\mathbf6$\). Warzanowski, P.; Magnaterra, M.; Sahle, Ch. J.; Moretti Sala, M.; Becker, P.; Bohatý, L.; Císařová, I.; Monaco, G.; Lorenz, T.; van Loosdrecht, P. H. M.; van den Brink, J.; Grüninger, M. (2024). 110(19) 195120.
• Noncollinear magnetic structures in the chiral antiperovskite β-Fe\($_\mathbf2$\)SeO. Qureshi, Navid; Morrow, Ryan; Eltoukhy, Samar; Grinenko, Vadim; Guilherme Buzanich, Ana; Onykiienko, Yevhen A.; Kulbakov, Anton; Inosov, Dmytro S.; Adler, Peter; Valldor, Martin (2024). 63(48) 22712–22720.
• The Lorenz ratio as a guide to scattering contributions to transport in strongly correlated metals. Sun, Fei; Mishra, Simli; Stockert, Ulrike; Daou, Ramzy; Kikugawa, Naoki; Perry, Robin S.; Hassinger, Elena; Hartnoll, Sean A.; Mackenzie, Andrew P.; Sunko, Veronika (2024). 121(35) e2318159121-.

  In many strongly correlated metals, resistivities rise more slowly than the Fermi liquid predictions but continue without saturation to very high temperatures, behavior of interest in condensed matter physics and beyond. For example, observations on the scattering rates of such metals are of relevance for comparison with cold atomic systems and the quark?gluon plasma. An open debate is the scattering mechanism that determines the observed phenomena. Using innovative experimental techniques to probe thermal transport in some representative strongly correlated oxide metals, we show that electron?electron scattering dominates, even at room temperature. This emphasizes potential universalities across different fields and guides the quest to understand potential quantum limits to strong scattering in many-body systems. In many physical situations in which many-body assemblies exist at temperature T, a characteristic quantum-mechanical time scale of approximately ?/kBT can be identified in both theory and experiment, leading to speculation that it may be the shortest meaningful time in such circumstances. This behavior can be investigated by probing the scattering rate of electrons in a broad class of materials often referred to as ?strongly correlated metals?. It is clear that in some cases only electron?electron scattering can be its cause, while in others it arises from high-temperature scattering of electrons from quantized lattice vibrations, i.e., phonons. In metallic oxides, which are among the most studied materials, analysis of electrical transport does not satisfactorily identify the relevant scattering mechanism at ?high? temperatures near room temperature. We therefore employ a contactless optical method to measure thermal diffusivity in two Ru-based layered perovskites, Sr3Ru2O7 and Sr2RuO4, and use the measurements to extract the dimensionless Lorenz ratio. By comparing our results to the literature data on both conventional and unconventional metals, we show how the analysis of high-temperature thermal transport can both give important insight into dominant scattering mechanisms and be offered as a stringent test of theories attempting to explain anomalous scattering.
• Magnetotransport in a graphite cylinder under quantizing fields. Kunchur, N.; Galeski, S.; Menges, F.; Wawrzyńczak, R.; Felser, C.; Meng, T.; Gooth, J. (2024). 110(19) 195141.
• Topological semimetals with intrinsic chirality as spin-controlling electrocatalysts for the oxygen evolution reaction. Wang, Xia; Yang, Qun; Singh, Sukriti; Borrmann, Horst; Hasse, Vicky; Yi, Changjiang; Li, Yongkang; Schmidt, Marcus; Li, Xiaodong; Fecher, Gerhard H.; Zhou, Dong; Yan, Binghai; Felser, Claudia (2024).

  Electrocatalytic water splitting is a promising approach for clean hydrogen production, but the process is hindered by the sluggish kinetics of the anodic oxygen evolution reaction (OER) owing to the spin-dependent electron transfer process. Efforts to control spin through chirality and magnetization have shown potential in enhancing OER performance. Here we harnessed the potential of topological chiral semimetals (RhSi, RhSn and RhBiS) and their spin-polarized Fermi surfaces to promote the spin-dependent electron transfer in the OER, addressing the traditional volcano-plot limitations. We show that OER activities follow the trend RhSithinspace
• GeaVR: An open-source tools package for geological-structural exploration and data collection using immersive virtual reality. Bonali, Fabio Luca; Vitello, Fabio; Kearl, Martin; Tibaldi, Alessandro; Whitworth, Malcolm; Antoniou, Varvara; Russo, Elena; Delage, Emmanuel; Nomikou, Paraskevi; Becciani, Ugo; van Wyk de Vries, Benjamin; Krokos, Mel (2024). 21 100156.

  We introduce GeaVR, an open-source package containing tools for geological-structural exploration and mapping in Immersive Virtual Reality (VR). GeaVR also makes it possible to carry out quantitative data collection on 3D realistic, referenced and scaled Virtual Reality scenarios. Making use of Immersive Virtual Reality technology through the Unity game engine, GeaVR works with commercially available VR equipment. This allows VR to be accessible to a broad audience, resulting in a revolutionary tool package for Earth Sciences. Users can explore various 3D datasets, spanning from freely available Digital Surface Models and Bathymetric data to ad-hoc 3D high-resolution models from photogrammetry processing. The user can navigate the 3D model in first person, walking or flying above the surrounding environment, mapping the main geological features such as points, lines and polygons, and collecting quantitative data using the provided field survey tools. Such data, including geographic coordinates, can be exported for further spatial analyses. Here we describe three different case studies aimed at showing the potential of our tools. GeaVR is revolutionary as it can be used worldwide, with no spatial limitations, both for geo-education and Earth Science popularization, as well as for research purposes. Secondly, it makes it possible to safely access dangerous areas, such as vertical cliffs or volcanic terrains, virtually from a computer screen or Virtual Reality headset. Furthermore, it can help to reduce carbon emissions by avoiding the use of flights and vehicles to conduct field trips.
• Photoinduced spin centers in photocatalytic metal–organic framework UiO-66. Kultaeva, Anastasiia; Biktagirov, Timur; Sperlich, Andreas; Dörflinger, Patrick; Calvo, Mauricio E.; Otal, Eugenio; Dyakonov, Vladimir (2024). n/a(n/a) 2413297.

  Abstract Metal–Organic frameworks (MOFs) are promising candidates for advanced photocatalytically active materials. These porous crystalline compounds have large active surface areas and structural tunability and are thus highly competitive with oxides, the well-established material class for photocatalysis. However, due to their complex organic and coordination chemistry composition, photophysical mechanisms involved in the photocatalytic processes in MOFs are still not well understood. Employing electron paramagnetic resonance (EPR) spectroscopy and time-resolved photoluminescence spectroscopy (trPL), the fundamental processes of electron and hole generation are investigated, as well as capture events that lead to the formation of various radical species in UiO-66, an archetypical MOF photocatalyst. A manifold of photoinduced electron spin centers is detected, which is subsequently analyzed and identified with the help of density-functional theory (DFT) calculations. Under UV illumination, the symmetry, g-tensors, and lifetimes of three distinct contributions are revealed: a surface O2-radical, a light-induced electron-hole pair, and a triplet exciton. Notably, the latter is found to emit (delayed) fluorescence. The findings provide new insights into the photoinduced charge transfer processes, which are the basis of photocatalytic activity in UiO-66. This sets the stage for further studies on photogenerated spin centers in this and similar MOF materials.
• Giant topological Hall effect and colossal magnetoresistance in Heusler ferromagnet near room temperature. Yanda, Premakumar; Noohinejad, Leila; Mao, Ning; Peshcherenko, Nikolai; Imasato, Kazuki; Srivastava, Abhay K.; Guan, Yicheng; Giri, Bimalesh; Sharma, Avdhesh Kumar; Manna, Kaustuv; Parkin, Stuart S. P.; Zhang, Yang; Shekhar, Chandra; Felser, Claudia (2024). n/a(n/a) 2411240.

  Abstract Colossal magnetoresistance (CMR) is an exotic phenomenon that allows for the efficient magnetic control of electrical resistivity and has attracted significant attention in condensed matter due to its potential for memory and spintronic applications. Heusler alloys are the subject of considerable interest in this context due to the electronic properties that result from the nontrivial band topology. Here, the observation of CMR near room temperature is reported in the shape memory Heusler alloy Ni2Mn1.4In0.6, which is attributed to the combined effects of magnetic field-induced martensite twin variant reorientation (MFIR) and magnetic field-induced structural phase transformation (MFIPT). This compound undergoes a structural phase transition from a cubic (austenite-L21) ferromagnetic (FM) to a monoclinic (martensite) antiferromagnetic (AFM), which leads to an effective increase in the size of the Fermi surface and consequently in CMR. Additionally, it exhibits significant anomalous Hall conductivity in both antiferromagnetic and ferromagnetic phases. Furthermore, it demonstrates a giant topological Hall resistivity (THR) ρyxT\\($\rho \_\mathrmyx^\mathrmT\$\) ≈6 µΩ.cm in the vicinity of martensite transition due to the enhanced spin chirality resulting from the formation of magnetic domains with Bloch-type domain walls. The findings contribute to the understanding of the magnetotransport of Ni-Mn-In Heusler alloys, which are prospective candidates for room-temperature spintronic applications.
• A zero external magnetic field quantum standard of resistance at the 10\($^\mathbf-9$\) level. Patel, D. K.; Fijalkowski, K. M.; Kruskopf, M.; Liu, N.; Götz, M.; Pesel, E.; Jaime, M.; Klement, M.; Schreyeck, S.; Brunner, K.; Gould, C.; Molenkamp, L. W.; Scherer, H. (2024).

  The quantum anomalous Hall effect is of potential use in metrology as it provides access to Hall resistance quantization in terms of the von Klitzing constant (RK\thinspace=\thinspaceh/e2, where h is Planck's constant and e the elementary charge) at zero external magnetic field. However, accessing the effect is challenging because it requires low temperatures (typically below 50\thinspacemK) and low bias currents (typically below 1\thinspace\textmuA). Here we report Hall resistance quantization measurements in the quantum anomalous Hall effect regime on a device based on the magnetic topological insulator V-doped (Bi,Sb)2Te3. We show that the relative deviation of the Hall resistance from RK at zero external magnetic field is (4.4\thinspace\textpm\thinspace8.7)\thinspacen\($\Omega$\)\thinspace\($\Omega$\)−1 when extrapolated to zero measurement current and (8.6\thinspace\textpm\thinspace6.7)\thinspacen\($\Omega$\)\thinspace\($\Omega$\)−1 when extrapolated to zero longitudinal resistivity (each with combined standard uncertainty, k\thinspace=\thinspace1). This precision and accuracy at the n\($\Omega$\)\thinspace\($\Omega$\)−1 level (or 10−9 of relative uncertainty) reach the thresholds required for relevant metrological applications and establish a zero external magnetic field quantum standard of resistance, which is necessary for the integration of quantum-based voltage and resistance standards into a single universal quantum electrical reference.
• Digitalisierung, KI und Pflege: Auf der Suche nach der digitalen Dividende Bringmann, Julia; Evans-Borchers, Michaela (2024).

  Julia Bringmann und Michaela Evans-Borchers analysieren den Forschungsstand in Bezug auf die Entlastung Pflegender durch digitale Innovationen und KI-Anwendungen und skizzieren die Voraussetzungen, damit Zeitgewinne und Entlastung realisiert werden können.
• The Digitalization of Healthcare for Older Adults: Digitalization as an Answer to Substantial Challenges in our Healthcare System Herrmann, Wolfram J.; Leser, Ulf; Möller, Sebastian; Voigt-Antons, Jan-Niklas; Gellert, Paul (2024). Berlin Universities Publishing, Berlin.

  Demographic change poses substantial challenges for our healthcare system and its ability to sustain good healthcare for older adults. Digitalization is one answer to this challenge. In this collection, we present different projects and approaches tackling the digitalization of healthcare for older adults based on a workshop conducted in Berlin in July 2023.
• Defining Knowledge: Bridging Epistemology and Large Language Models. Fierro, Constanza; Dhar, Ruchira; Stamatiou, Filippos; Garneau, Nicolas; Sogaard, Anders Y. Al-Onaizan, M. Bansal, Y.-N. Chen (eds.) (2024). 16096–16111.

  Knowledge claims are abundant in the literature on large language models (LLMs); but can we say that GPT-4 truly ``knows'' the Earth is round? To address this question, we review standard definitions of knowledge in epistemology and we formalize interpretations applicable to LLMs. In doing so, we identify inconsistencies and gaps in how current NLP research conceptualizes knowledge with respect to epistemological frameworks. Additionally, we conduct a survey of 100 professional philosophers and computer scientists to compare their preferences in knowledge definitions and their views on whether LLMs can really be said to know. Finally, we suggest evaluation protocols for testing knowledge in accordance to the most relevant definitions.
• Leveraging User-Generated Metadata of Online Videos for Cover Song Identification. Hachmeier, Simon; Jäschke, Robert (2024).
• A Comparative Study of Cooperative and Non-cooperative Wideband Spectrum Sensing in Cognitive Radio Networks for 5g Applications. Dike, Blessing C. (2024). (Vol. 16) 1–19.

  The rapid advancements in 5G technologies have created an unprecedented need for efficient spectrum utilization to support increasing data traffic and diverse communication services. In this context, accurate and reliable spectrum sensing is essential. This study explores wideband spectrum sensing strategies, comparing non-cooperative cognitive radio (CR) techniques with cooperative methods across multiple subbands. A novel cooperative wideband spectrum sensing framework was developed, incorporating a K-outof-N fusion rule at the fusion center to make optimal decisions by selecting an appropriate K for a given number of cooperating CRs. This approach addresses noise uncertainty, a common challenge in traditional non-cooperative energy detection methods, particularly in 5G environments under Additive White Gaussian Noise (AWGN) conditions, assumed to be identically and independently distributed (i.i.d.). However, while cooperative sensing significantly improves detection in low signal-to-noise ratio (SNR) scenarios with higher false alarm rates (between 0.5 and 1), our findings reveal that it does not consistently outperform non-cooperative methods at very low false alarm rates (0.01 and 0.1) under poor SNR conditions. These findings highlight the need for further research to enhance cooperative sensing strategies for various operational environments.
• Conceptual Data Scaling in Machine Learning. Technical Report (PhD dissertation), Hirth, Johannes (2024).

  Information that is intended for human interpretation is frequently represented in a structured manner. This allows for a navigation between individual pieces to find, connect or combine information to gain new insights. Within a structure, we derive knowledge from inference of hierarchical or logical relations between data objects. For unstructured data there are numerous methods to define a data schema based on user interpretations. Afterward, data objects can be aggregated to derive (hierarchical) structures based on common properties. There are four main challenges with respect to the explainability of the derived structures. First, formal procedures are needed to infer knowledge about the data set, or parts of it, from hierarchical structures. Second, what does knowledge inferred from a structure imply for the data set it was derived from? Third, structures may be incomprehensibly large for human interpretation. Methods are needed to reduce structures to smaller representations in a consistent, comprehensible manner that provides control over possibly introduced error. Forth, the original data set does not need to have interpretable features and thus only allow for the inference of structural properties. In order to extract information based on real world properties, we need methods that are able to add such properties. With the presented work, we address these challenges using and extending the rich tool-set of Formal Concept Analysis. Here, data objects are aggregated to closed sets called formal concepts based on (unary) symbolic attributes that they have in common. The process of deriving symbolic attributes is called conceptual scaling and depends on the interpretation of the data by the analyst. The resulting hierarchical structure of concepts is called concept lattice. To infer knowledge from the concept lattice structures we introduce new methods based on sub-structures that are of standardized shape, called ordinal motifs. This novel method allows us to explain the structure of a concept lattice based on geometric aspects. Throughout our work, we focus on data representations from multiple state-of-the-art machine learning algorithms. In all cases, we elaborate extensively on how to interpret these models through derived concept lattices and develop scaling procedures specific to each algorithm. Some of the considered models are black-box models whose internal data representations are numeric with no clear real world semantics. For these, we present a method to link background knowledge to the concept lattice structure. To reduce the complexity of concept lattices we provide a new theoretical framework that allows us to generate (small) views on a concept lattice. These enable more selective and comprehensibly sized explanations for data parts that are of interest. In addition to that, we introduce methods to combine and subtract views from each other, and to identify missing or incorrect parts.
• Disclosing Diverse Perspectives of News Articles for Navigating between Online Journalism Content. Horn, Viktoria; Hirth, Johannes; Holfeld, Julian; Behmenburg, Jens Hendrik; Draude, Claude; Stumme, Gerd in NordiCHI 2024 (2024).

  Today, exposure to journalistic online content is predominantly controlled by news recommender systems, which often suggest content that matches user’s interests or is selected according to non-transparent recommendation criteria. To circumvent resulting trade-offs like polarisation or fragmentation whilst ensuring user’s autonomy, we explore how different perspectives within online news can be disclosed instead for guiding navigation. To do so, we developed an interactive prototype that displays article titles in correspondence to their argumentative orientation. In order to investigate how the usage of our novel navigation structure impacts the choice of news articles and user experience, we conducted an exploratory user study assessing the impact of the design parameters chosen. Implications are drawn from the study results and the development of the interactive prototype for the exposure to diversity in the context of navigating news content online.
• The Birkhoff Completion of Finite Lattices. Abdulla, Mohammad; Hirth, Johannes; Stumme, Gerd I. P. Cabrera, S. Ferré, S. Obiedkov (eds.) (2024). 20–35.

  We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.
• Conceptual Mapping of Controversies. Draude, Claude; Dürrschnabel, Dominik; Hirth, Johannes; Horn, Viktoria; Kropf, Jonathan; Lamla, Jörn; Stumme, Gerd; Uhlmann, Markus I. P. Cabrera, S. Ferré, S. Obiedkov (eds.) (2024). 201–216.

  With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles.
• Verrechnung – Design – Kultivierung: Instrumentenkasten für die Gestaltung fairer Geschäftsmodelle durch Ko-Valuation. Draude, Claude; Engert, Simon; Hess, Thomas; Hirth, Johannes; Horn, Viktoria; Kropf, Jonathan; Lamla, Jörn; Stumme, Gerd; Uhlmann, Markus; Zwingmann, Nina (M. Friedewald; A. Roßnagel; C. Geminn; M. Karaboga, eds.) (2024, March).
• The Geometric Structure of Topic Models. Hirth, Johannes; Hanika, Tom (2024).
• Einsatzmöglichkeiten von Kleinsatelliten zur Erforschung von erdnahen Asteroiden am Beispiel von (99942) Apophis. Männel, J.; Ali, F.; Amza, A.; Herbst, T.; Kayal, H.; Neumann, T.; Riegler, C. (2024).

  Bei der Erforschung von Asteroiden kommen zunehmend auch Kleinsatelliten zum Einsatz. Sie werden sowohl als eigenständige Missionen aber auch zur Missionsunterstützung eingesetzt. In Vorbereitung auf den erdnahen Vorbeiflug des Asteroiden (99942) Apophis am 13.04.2029 wurden zahlreiche Missionskonzepte vorgeschlagen, welche den Einsatz von Kleinsatelliten vorsehen. Im Rahmen des laufenden Vorhabens NEAlight (FKZ 50OO2413) werden drei verschiedene Optionen für eine nationale Kleinsatellitenmission zur Erforschung von Apophis (99942) Apophis erarbeitet und deren Machbarkeit analysiert. Die Szenarien umfassen dabei einen CubeSat als Ergänzung zur europäischen RAMSES Mission, Kleinsatelliten zur Demonstration von Flyby Untersuchungen und eine eigenständige Rendevous Mission.
• Erste Flugergebnisse der Satellitenmission SONATE-2. Balagurin, O.; Greiner, T.; Herbst, T.; Kaiser, T.; Kayal, H.; Männel, J.; Maurer, A.; Neumann, T.; Riegler, C.; Schwarz, T. (2024).

  SONATE-2 ist eine Technologiedemonstrationsmission im 6U -CubeSat-Format, deren Hauptziel es ist, neuartige Methoden der künstlichen Intelligenz für den Einsatz auf Nanosatelliten im Orbit zu verifizieren. Nach etwa drei Jahren Entwicklungszeit wurde der SONATE-2-Satellit Anfang März 2024 durch eine Falcon-9-Rakete in den Low Earth Orbit befördert und wird seitdem vom eigenen Missionskontrollzentrum der Uni-versität Würzburg betrieben. Dieses Paper fasst die Ergebnisse der ersten fünf Monate des Satellitenbe-triebs zusammen.
• In-Orbit Validierung des MultiView-Sternsensors auf dem SONATE-2 CubeSat. Neumann, T.; Balagurin, O.; Kaiser, T.; Kayal, H.; Maurer, A.; Riegler, C.; Schwarz, T. (2024).

  Es wurde ein mehrköpfiger Sternsensor für CubeSats für den Einsatz im sog. TunaCanSlot konzipiert. Der Sensor zeichnet sich durch die gegebene Redundanz auf Grund der Verwendung einer Vielzahl an Sensorköpfen trotz des geringen Bauvolumens aus. Im Falle eines Ausfalls oder bei fehlenden Messdaten werden die Sensoren autonom umgeschaltet, die so die Verfügbarkeit der Lagedaten erhöht. Der MultiViewSternsensor wurde an Bord des SONATE2 Nanosatelliten Anfang März 2024 in die Erdumlaufbahn gebracht. Neben anderen Subsystemen als Technologiedemonstration, wurde der Sensor während der Missionslaufzeit in Betrieb genommen und bereits mehreren Tests unterzogen. Damit wird die Raumfahrttauglichkeit sowie der Einsatz im LowEarth Orbit nachgewiesen. In diesem Paper werden die für die Validierung benötigten Aspekte dargestellt. Wichtig sind die Überprüfungen sowohl der Grundfunktionen als Subsystem gegenüber dem Satelliten, als auch der Messprozesse der Sensorköpfe selbst. Die bisherigen Ergebnisse der InOrbitTests werden in Verbindung mit den dargestellten Validierungsaspekten präsentiert. Sie bestätigen die Funktion des Konzeptes und bilden bereits eine gute Grundlage für die in Aussicht gestellte Validierung auf TRL 9.
• Realization of Hilbert space fragmentation and fracton dynamics in two dimensions. Will, Melissa; Moessner, Roderich; Pollmann, Frank (2024). 133(19) 196301.
• Magnetic domain structure of ferromagnetic Tb(0001) films. Härtl, Patrick; Leisegang, Markus; Bode, Matthias (2024). 110(18) 184405.
• Changing tasks and changing public servants? The digitalisation and automation of public administrative work. Gräfe, Philipp; Marienfeldt, Justine; Wehmeier, Liz Marla; Kuhlmann, Sabine (2024).

  Due to digitalisation, automation, and the use of artificial intelligence in policy implementation, public administrations are adapting their workflows and organisational structures. We explore the impact of the digital transformation on the nature of tasks as well as the subsequent (potentially) changing demand for competences and job profiles in public executive agencies, based on a comparative case study and semi-structured interviews with over 60 public servants from three key administrative services in Germany – electronic tax returns, internet-based vehicle registrations, and digital building applications. The findings reveal less complex tasks being partially substituted and core discretionary tasks being standardised. We highlight the emergence of “work for digitalisation” that often remains “invisible”, including “compensating work” (scanning, IT support), “connecting work” (software maintenance) and “cleaning work” (validating data), resulting in an overall increased workload. Additionally, there is a need for role-specific new competences and upskilling of middle-level public servants. Our results indicate a potential substitution of traditional administrative job profiles but a growing demand for highly skilled, academically trained public servants. The findings therefore challenge the notion that digitalisation and automation threaten human labour or effectively counteract staff shortages. Rather, (potential) benefits, burdens, and job perspectives are distributed unevenly across job profiles.
• OtterlyObsessedWithSemantics at SemEval-2024 Task 4: Developing a Hierarchical Multi-Label Classification Head for Large Language Models. Wunderle, Julia; Schubert, Julian; Cacciatore, Antonella; Zehe, Albin; Pfister, Jan; Hotho, Andreas A. K. Ojha, A. S. Dougruöz, H. Tayyar Madabushi, G. Da San Martino, S. Rosenthal, A. Rosá (eds.) (2024). 602–612.

  For our submission for Subtask 1, we developed a custom classification head that is designed to be applied atop of a Large Language Model. We reconstructed the hierarchy across multiple fully connected layers, allowing us to incorporate previous foundational decisions in subsequent, more fine-grained layers. To find the best hyperparameters, we conducted a grid-search and to compete in the multilingual setting, we translated all documents to English.
• Adapting Sequential Recommender Models to Content Recommendation in Chat Data using Non-Item Page-Models. Zehe, Albin; Fischer, Elisabeth; Kaiser, Jonas; Wagner, Toni; Hotho, Andreas (2024).
• Event Analytics across Languages and Communities Marenzi, Ivana; Gottschalk, Simon; Müller-Budack, Eric; Tadić, Marko; Winters, Jane (I. Marenzi; S. Gottschalk; E. Müller-Budack; M. Tadić; J. Winters, eds.) (2024). Springer Cham.

  This open access book presents interdisciplinary and cross-sectoral research results fostering event analytics across languages and communities. It is based on the CLEOPATRA International Training Network, which explored how we analyze and understand the major events that influence and shape our lives and societies, and how they unfold online. This analysis was achieved through various case studies, the development of novel methodologies in fields such as data mining and natural language processing, and the creation of new event-centric datasets aggregated in the Open Event Knowledge Graph (OEKG), a multilingual event-centric knowledge graph that contains more than 1 million events in 15 languages. The book is divided into three parts, focusing on different aspects of event analytics across languages and communities: Part I Event-centric Multilingual and Multimodal NLP Technologies presents five chapters reporting on recent developments in NLP technologies required to process multilingual information. Next, the four chapters of Part II: Event-centric Multilingual Knowledge Technologies discuss technologies integrating multilingual event-centric information in knowledge graphs and providing user access to such information. Finally, Part III: Event Analytics covers three selected aspects of multilingual event analytics, namely an analysis of event-centric news spreading barriers, claim detection in social media, and the narrativization of events as a means of presenting event data. This book is mainly written for researchers in academia and industry, who work on topics like natural language processing, large language models, multilingual information retrieval or event analytics.
• Capacitive Thin-Film User Interfaces on Stones. Mwaita, Kevin Fred; Carrasco-Pena, Alejandro; Pretto, Niccolò; Preindl, Thomes; Pointner, Andreas; Münzenrieder, Niko; Haller, Michael (2024). 1–3.

  Integrating Abstract-Integrating interactive interfaces into everyday items is crucial for connecting the digital and physical worlds. However, many current interface technologies are obtrusive and difficult to integrate into the environment. This work presents eStone, an approach where natural stone surfaces are used as substrates for capacitive thin-film sensors acting as interactive interfaces. metallic thin films such as 200 nm-thick aluminum (AI) were sputter deposited and structured on a range of different stones, including marble, granite, porphyry, and limestone, creating conductive patterns and enabling the realization of capacitive sensors. The nanometer-scale thickness of these layers preserves the stone's natural appearance and texture and transforms them into functionalized and durable capacitive sensors. Combined with through-hole contacting vias and customized conventional readout electronics, e Stone demonstrates excellent performance when used as a capacitive controller to adjust the volume of a loudspeaker or the temperature of a thermostat.
• Towards the development of an automated robotic storyteller: comparing approaches for emotional story annotation for non-verbal expression via body language. Steinhaeusser, Sophia C; Zehe, Albin; Schnetter, Peggy; Hotho, Andreas; Lugrin, Birgit (2024). 1–23.
• Schrausser/Various_programs: 3.5. Schrausser, Dietmar Gerald (2024, December).

  Tools for simple calculations, bootstrapping, permutation tests etc.
• Peer Review Report For: Co-creation of the Digital Democracy and Data Commons Manifesto: alternative sociotechnical visions of data [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved]. Draude, Claude (2024). 4(45)
• Peer Review Report For: Co-creation of the Digital Democracy and Data Commons Manifesto: alternative sociotechnical visions of data [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved]. Draude, Claude (2024). 4(45)
• Peer Review Report For: Co-creation of the Digital Democracy and Data Commons Manifesto: alternative sociotechnical visions of data [version 2; peer review: 3 approved, 1 approved with reservations, 1 not approved]. Draude, Claude (2024). 4(45)
• Evaluating Water-Energy Nexus related Water Saving Potentials of Residential Households with Solar PV and Electric Storage. Schiller, Jonas; Pruckner, Marco in BuildSys ’24 (2024). 159–163.

  The energy sector is one of the largest consumers of water in Europe. As water scarcity has a severe impact on nature and human health, energy related water consumption, similar as for CO2 emissions, should be included in an environmental assessment. This study investigates the indirect water usage of households through their energy consumption based on a case study for two European countries, Germany and Spain. We calculate the water consumption of several thousand individual residential households based on their electricity consumption and the water intensity of the electric grid in an hourly resolution. Additionally, we investigate the effect of residential photovoltaic and battery electric storage systems on the water consumption and contrast potential optimization strategies towards high self-consumption and water consumption minimization. Our results show that the introduction of a small 0.89 kWp solar system can already reduce indirect water consumption by on average 20 % while a battery and solar photovoltaic system optimized for minimal water consumption achieves a mean reduction of over 40 % in Spain and 25 % in Germany.
• Prompting for Perks: Enhancing Generative AI-Enabled Job Crafting in Knowledge Work. Ritz, Eva; Mühlheim, Joël; Freise, Leonie Rebecca; Li, Mahei Manhei (2024).

  The increasing use of Generative AI (GAI) has captivated industries seeking to enhance productivity, particularly among white-collar workers. Drawing upon job crafting theory from occupational psychology, prior research suggests that GAI applications can also enhance employee well-being by modifying their work environment for self-perceived benefits. However, the conditions facilitating job crafting behaviours in this context remain underexplored. This study posits that prompt engineering can promote job crafting behaviour. To explore this, we conducted a between-subjects online experiment, manipulating a prompt support intervention through worked examples. Additionally, we examined the moderating role of participants' AI literacy in influencing the effectiveness of the prompt intervention. A pre-test with 42 participants indicated that prompt support can increase GAI-enabled job crafting, but only for those with high AI literacy. The findings underscore the importance of AI literacy and prompt engineering in optimising job crafting behaviour, advocating for organisational training programs to support these skills.
• Einsatzmöglichkeiten von Kleinsatelliten zur Erforschung von erdnahen Asteroiden am Beispiel von (99942) Apophis. Männel, J.; Ali, F.; Amza, A.; Herbst, T.; Kayal, H.; Neumann, T.; Riegler, C. (2024).

  Bei der Erforschung von Asteroiden kommen zunehmend auch Kleinsatelliten zum Einsatz. Sie werden sowohl als eigenständige Missionen aber auch zur Missionsunterstützung eingesetzt. In Vorbereitung auf den erdnahen Vorbeiflug des Asteroiden (99942) Apophis am 13.04.2029 wurden zahlreiche Missionskonzepte vorgeschlagen, welche den Einsatz von Kleinsatelliten vorsehen. Im Rahmen des laufenden Vorhabens NEAlight (FKZ 50OO2413) werden drei verschiedene Optionen für eine nationale Kleinsatellitenmission zur Erforschung von Apophis (99942) Apophis erarbeitet und deren Machbarkeit analysiert. Die Szenarien umfassen dabei einen CubeSat als Ergänzung zur europäischen RAMSES Mission, Kleinsatelliten zur Demonstration von Flyby Untersuchungen und eine eigenständige Rendevous Mission.
• Erste Flugergebnisse der Satellitenmission SONATE-2. Balagurin, O.; Greiner, T.; Herbst, T.; Kaiser, T.; Kayal, H.; Männel, J.; Maurer, A.; Neumann, T.; Riegler, C.; Schwarz, T. (2024).

  SONATE-2 ist eine Technologiedemonstrationsmission im 6U -CubeSat-Format, deren Hauptziel es ist, neuartige Methoden der künstlichen Intelligenz für den Einsatz auf Nanosatelliten im Orbit zu verifizieren. Nach etwa drei Jahren Entwicklungszeit wurde der SONATE-2-Satellit Anfang März 2024 durch eine Falcon-9-Rakete in den Low Earth Orbit befördert und wird seitdem vom eigenen Missionskontrollzentrum der Uni-versität Würzburg betrieben. Dieses Paper fasst die Ergebnisse der ersten fünf Monate des Satellitenbe-triebs zusammen.
• Aspirin for Secondary Prevention of Atherosclerosis—Evidence or Dogma?. Cleland, John G. F. (2024).
• Risk of Intracranial Hemorrhage Associated With Direct Oral Anticoagulation vs Antiplatelet Therapy: A Systematic Review and Meta-Analysis. Coyle, Mark; Lynch, Amy; Higgins, Meave; Costello, Maria; Judge, Conor; O’Donnell, Martin; Reddin, Catriona (2024). 7(12) e2449017.
• Synaptic connectome of the Drosophila circadian clock. Reinhard, Nils; Fukuda, Ayumi; Manoli, Giulia; Derksen, Emilia; Saito, Aika; Möller, Gabriel; Sekiguchi, Manabu; Rieger, Dirk; Helfrich-Förster, Charlotte; Yoshii, Taishi; Zandawala, Meet (2024). 15(1) 10392.
• Ten simple rules for designing graphical abstracts. Jambor, Helena Klara; Bornhäuser, Martin (2024). 20(2) 1–12.

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• Scaling Dimension. Ganter, Bernhard; Hanika, Tom; Hirth, Johannes in Lecture Notes in Computer Science, D. Dürrschnabel, D. López-Rodríguez (eds.) (2023). (Vol. 13934) 64–77.
• Underwater 3D Reconstruction from Video or Still Imagery: Matisse and 3DMetrics Processing and Exploitation Software. Arnaubec, Aurélien; Ferrera, Maxime; Escartín, Javier; Matabos, Marjolaine; Gracias, Nuno; Opderbecke, Jan (2023). 11(5)

  This paper addresses the lack of “push-button” software for optical marine imaging, which currently limits the use of photogrammetric approaches by a wider community. It presents and reviews an open source software, Matisse, for creating textured 3D models of complex underwater scenes from video or still images. This software, developed for non-experts, enables routine and efficient processing of underwater images into 3D models that facilitate the exploitation and analysis of underwater imagery. When vehicle navigation data are available, Matisse allows for seamless integration of such data to produce 3D reconstructions that are georeferenced and properly scaled. The software includes pre-processing tools to extract images from videos and to make corrections for color and uneven lighting. Four datasets of different 3D scenes are provided for demonstration. They include both input images and navigation and associated 3D models generated with Matisse. The datasets, captured under different survey geometries, lead to 3D models of different sizes and demonstrate the capabilities of the software. The software suite also includes a 3D scene analysis tool, 3DMetrics, which can be used to visualize 3D scenes, incorporate elevation terrain models (e.g., from high-resolution bathymetry data) and manage, extract, and export quantitative measurements for the 3D data analysis. Both software packages are publicly available.
• Automatic Textual Explanations of Concept Lattices. Hirth, Johannes; Horn, Viktoria; Stumme, Gerd; Hanika, Tom M. Ojeda-Aciego, K. Sauerwald, R. Jäschke (eds.) (2023). 138–152.

  Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.
• Conceptual Views on Tree Ensemble Classifiers. Hanika, Tom; Hirth, Johannes (2023). 108930.

  Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.
• Student evaluations of using virtual reality to investigate natural hazard field sites. Wright, Paul N; Whitworth, Malcolm; Tibaldi, Alessandro; Bonali, Fabio; Nomikou, Paraskevi; Antoniou, Vavara; Vitello, Fabio; Becciani, Ugo; Krokos, Mel; de Vries, Benjamin Van Wyk (2023). 47(2) 311–329.
• Ordinal Motifs in Lattices. Hirth, Johannes; Horn, Viktoria; Stumme, Gerd; Hanika, Tom (2023). 120009.

  Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose /ordinal motifs/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.
• Copy Suppression: Comprehensively Understanding an Attention Head. McDougall, Callum; Conmy, Arthur; Rushing, Cody; McGrath, Thomas; Nanda, Neel (2023).
• Chest X-ray analysis empowered with deep learning: A systematic review. Meedeniya, Dulani; Kumarasinghe, Hashara; Kolonne, Shammi; Fernando, Chamodi; Díez, Isabel De la Torre; Marques, Gonçalo (2022). 126 109319.
• Knowledge cores in large formal contexts. Hanika, Tom; Hirth, Johannes (2022).

  Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.
• Research Topic Flows in Co-Authorship Networks. Schäfermeier, Bastian; Hirth, Johannes; Hanika, Tom (2022).

  In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.
• The use of immersive virtual reality for teaching fieldwork skills in complex structural terrains. Harknett, J.; Whitworth, M.; Rust, D.; Krokos, M.; Kearl, M.; Tibaldi, A.; Bonali, F.L.; Van Wyk de Vries, B.; Antoniou, V.; Nomikou, P.; Reitano, D.; Falsaperla, S.; Vitello, F.; Becciani, U. (2022). 163 104681.

  Innovations in virtual reality (VR) technology have led to exciting possibilities in teaching earth sciences, allowing students to experience complex geological sites that, due to cost and logistical reasons, they would not normally be able to experience. The need for high quality online digital learning resources and blended learning was brought to the forefront during the SARS-CoV-2 pandemic, as courses with a traditional physical field work component were forced to move online and provide alternatives to students. While it is unlikely that virtual field trips (VFT) would be accepted by students as a replacement of real-world fieldwork moving out of the pandemic, research shows promise that using IVR experiences can lead to enhanced learning outcomes in geosciences, warranting its inclusion on the curricula. This paper presents the outputs of a project to improve student learning in complex geological environments using VR. Here we outline a workflow that was developed to collect high resolution imagery using remote sensing to create digital outcrop models (DOM) of complex geological sites. Using this framework, this paper will then explore the use of VR for an investigation of the Husavik Triple Junction, a complex structural site in northern Iceland, explaining how the drone data was converted to a 3D DOM and demonstrating how VR can be used to simulate real world field mapping. Finally, we describe how these IVR activities have been integrated into taught modules at postgraduate level and discuss how the use of IVR experiences can complement existing geoscience curriculum design.
• How Academics and the Public Experienced Immersive Virtual Reality for Geo-Education. Bonali, Fabio L.; Russo, Elena; Vitello, Fabio; Antoniou, Varvara; Marchese, Fabio; Fallati, Luca; Bracchi, Valentina; Corti, Noemi; Savini, Alessandra; Whitworth, Malcolm; Drymoni, Kyriaki; Mariotto, Federico Pasquaré; Nomikou, Paraskevi; Sciacca, Eva; Bressan, Sofia; Falsaperla, Susanna; Reitano, Danilo; van Wyk de Vries, Benjamin; Krokos, Mel; Panieri, Giuliana; Stiller-Reeve, Mathew Alexander; Vizzari, Giuseppe; Becciani, Ugo; Tibaldi, Alessandro (2022). 12(1)

  Immersive virtual reality can potentially open up interesting geological sites to students, academics and others who may not have had the opportunity to visit such sites previously. We study how users perceive the usefulness of an immersive virtual reality approach applied to Earth Sciences teaching and communication. During nine immersive virtual reality-based events held in 2018 and 2019 in various locations (Vienna in Austria, Milan and Catania in Italy, Santorini in Greece), a large number of visitors had the opportunity to navigate, in immersive mode, across geological landscapes reconstructed by cutting-edge, unmanned aerial system-based photogrammetry techniques. The reconstructed virtual geological environments are specifically chosen virtual geosites, from Santorini (Greece), the North Volcanic Zone (Iceland), and Mt. Etna (Italy). Following the user experiences, we collected 459 questionnaires, with a large spread in participant age and cultural background. We find that the majority of respondents would be willing to repeat the immersive virtual reality experience, and importantly, most of the students and Earth Science academics who took part in the navigation confirmed the usefulness of this approach for geo-education purposes.
• On the lattice of conceptual measurements. Hanika, Tom; Hirth, Johannes (2022). 613 453–468.

  We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.
• VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations. Nguyen, Ha Q.; Lam, Khanh; Le, Linh T.; Pham, Hieu H.; Tran, Dat Q.; Nguyen, Dung B.; Le, Dung D.; Pham, Chi M.; Tong, Hang T. T.; Dinh, Diep H.; Do, Cuong D.; Doan, Luu T.; Nguyen, Cuong N.; Nguyen, Binh T.; Nguyen, Que V.; Hoang, Au D.; Phan, Hien N.; Nguyen, Anh T.; Ho, Phuong H.; Ngo, Dat T.; Nguyen, Nghia T.; Nguyen, Nhan T.; Dao, Minh; Vu, Van (2022).
• Formal Conceptual Views in Neural Networks. Hirth, Johannes; Hanika, Tom (2022).
• Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning. Tiu, Ekin; Talius, Ellie; Patel, Pujan; Langlotz, Curtis P; Ng, Andrew Y; Rajpurkar, Pranav (2022). 6(12) 1399–1406.
• GPM: A graph convolutional network based reinforcement learning framework for portfolio management. Shi, Si; Li, Jianjun; Li, Guohui; Pan, Peng; Chen, Qi; Sun, Qing (2022). 498 14–27.

  Portfolio management is a decision-making process of periodically reallocating a certain amount of funds into a portfolio of assets, with the objective of maximizing the profits constrained to a given risk level. Due to its nature of learning from dynamic interactions and planning for long-run performance, reinforcement learning (RL) recently has received much attention in portfolio management. However, most of existing RL-based PM approaches in general only consider price changes of portfolio assets and the implicit correlations between price changes, while ignoring the rich relations between companies in the market, such as two assets in the same sector or two companies have a supplier-customer relation, which are very important for trading decision making. To address these limitations, in this paper, we propose GPM, a novel graph convolutional network-based reinforcement learning framework for portfolio management, which first employs Relational Graph Convolutional Network (R-GCN) to extract asset relational features, and then combines relational features with multi-scale temporal features to make trading decisions for better performance. We also introduce softmax with temperature to increase portfolio diversity, which leads to further increase in profits. Experimental results on two real-world datasets: NASDAQ and NYSE, validate the effectiveness of GPM over state-of-the-art PM methods. Further, more experiments are conducted to evaluate GPM with different graph neural networks and different number of network layers, to explore proper settings for different markets.
• AnoViT: Unsupervised Anomaly Detection and Localization With Vision Transformer-Based Encoder-Decoder. Lee, Yunseung; Kang, Pilsung (2022). 10 46717–46724.
• Virtual Geosite Communication through a WebGIS Platform: A Case Study from Santorini Island (Greece). Pasquaré Mariotto, Federico; Antoniou, Varvara; Drymoni, Kyriaki; Bonali, Fabio Luca; Nomikou, Paraskevi; Fallati, Luca; Karatzaferis, Odysseas; Vlasopoulos, Othonas (2021). 11(12)

  We document and show a state-of-the-art methodology that could allow geoheritage sites (geosites) to become accessible to scientific and non-scientific audiences through immersive and non-immersive virtual reality applications. This is achieved through a dedicated WebGIS platform, particularly handy in communicating geoscience during the COVID-19 era. For this application, we selected nine volcanic outcrops in Santorini, Greece. The latter are mainly associated with several geological processes (e.g., dyking, explosive, and effusive eruptions). In particular, they have been associated with the famous Late Bronze Age (LBA) eruption, which made them ideal for geoheritage popularization objectives since they combine scientific and educational purposes with geotourism applications. Initially, we transformed these stunning volcanological outcrops into geospatial models—the so called virtual outcrops (VOs) here defined as virtual geosites (VGs)—through UAV-based photogrammetry and 3D modeling. In the next step, we uploaded them on an online platform that is fully accessible for Earth science teaching and communication. The nine VGs are currently accessible on a PC, a smartphone, or a tablet. Each one includes a detailed description and plenty of annotations available for the viewers during 3D exploration. We hope this work will be regarded as a forward model application for Earth sciences’ popularization and make geoheritage open to the scientific community and the lay public.
• Deep oceanic submarine fieldwork with undergraduate students: an immersive experience with the Minerve software. Métois, M.; Martelat, J.-E.; Billant, J.; Andreani, M.; Escart’in, J.; Leclerc, F.; team, the ICAP (2021). 12(12) 2789–2802.
• DRAEM - A Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection. Zavrtanik, Vitjan; Kristan, Matej; Skovcaj, Danijel (2021). 8330–8339.
• Exploring Scale-Measures of Data Sets. Hanika, Tom; Hirth, Johannes in Lecture Notes in Computer Science, A. Braud, A. Buzmakov, T. Hanika, F. L. Ber (eds.) (2021). (Vol. 12733) 261–269.
• Quantifying the Conceptual Error in Dimensionality Reduction. Hanika, Tom; Hirth, Johannes in Lecture Notes in Computer Science, T. Braun, M. Gehrke, T. Hanika, N. Hernandez (eds.) (2021). (Vol. 12879) 105–118.
• INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection. Andresini, Giuseppina; Pendlebury, Feargus; Pierazzi, Fabio; Loglisci, Corrado; Appice, Annalisa; Cavallaro, Lorenzo in AISec ’21 (2021). 111–122.

  Despite decades of research in network traffic analysis and incredible advances in artificial intelligence, network intrusion detection systems based on machine learning (ML) have yet to prove their worth. One core obstacle is the existence of concept drift, an issue for all adversary-facing security systems. Additionally, specific challenges set intrusion detection apart from other ML-based security tasks, such as malware detection.In this work, we offer a new perspective on these challenges. We propose INSOMNIA, a semi-supervised intrusion detector which continuously updates the underlying ML model as network traffic characteristics are affected by concept drift. We use active learning to reduce latency in the model updates, label estimation to reduce labeling overhead, and apply explainable AI to better interpret how the model reacts to the shifting distribution.To evaluate INSOMNIA, we extend TESSERACT - a framework originally proposed for performing sound time-aware evaluations of ML-based malware detectors - to the network intrusion domain. Our evaluation shows that accounting for drifting scenarios is vital for effective intrusion detection systems.
• Roadmap on organic–inorganic hybrid perovskite semiconductors and devices. Schmidt-Mende, Lukas; Dyakonov, Vladimir; Olthof, Selina; Ünlü, Feray; Lê, Khan Moritz Trong; Mathur, Sanjay; Karabanov, Andrei D.; Lupascu, Doru C.; Herz, Laura M.; Hinderhofer, Alexander; Schreiber, Frank; Chernikov, Alexey; Egger, David A.; Shargaieva, Oleksandra; Cocchi, Caterina; Unger, Eva; Saliba, Michael; Byranvand, Mahdi Malekshahi; Kroll, Martin; Nehm, Frederik; Leo, Karl; Redinger, Alex; Höcker, Julian; Kirchartz, Thomas; Warby, Jonathan; Gutierrez-Partida, Emilio; Neher, Dieter; Stolterfoht, Martin; Würfel, Uli; Unmüssig, Moritz; Herterich, Jan; Baretzky, Clemens; Mohanraj, John; Thelakkat, Mukundan; Maheu, Clément; Jaegermann, Wolfram; Mayer, Thomas; Rieger, Janek; Fauster, Thomas; Niesner, Daniel; Yang, Fengjiu; Albrecht, Steve; Riedl, Thomas; Fakharuddin, Azhar; Vasilopoulou, Maria; Vaynzof, Yana; Moia, Davide; Maier, Joachim; Franckevičius, Marius; Gulbinas, Vidmantas; Kerner, Ross A.; Zhao, Lianfeng; Rand, Barry P.; Glück, Nadja; Bein, Thomas; Matteocci, Fabio; Castriotta, Luigi Angelo; Di Carlo, Aldo; Scheffler, Matthias; Draxl, Claudia (2021). 9(10)
• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization. Li, Chun-Liang; Sohn, Kihyuk; Yoon, Jinsung; Pfister, Tomas (2021). abs/2104.04015
• Virtual Geosites as Innovative Tools for Geoheritage Popularization: A Case Study from Eastern Iceland. Pasquaré Mariotto, Federico; Bonali, Fabio Luca (2021). 11(4)

  In this paper, we have adopted a modern, cutting-edge methodology to make geoheritage sites (geosites) available and explorable worldwide, through both immersive and non-immersive virtual reality, particularly suitable also in COVID-19 times. In doing this, we have focused our attention on five different outcroppings, shallow magma bodies in Iceland: such geological objects, although being often underestimated, are, on the contrary, very suitable for geoheritage popularization purposes. These outstanding outcrops have been transformed in virtual outcrops (VOs) through UAV-based photogrammetry 3D modelling, and have been uploaded on a brand-new, dedicated online resource (GeoVires Virtual Reality Lab for Earth Sciences) which is accessible worldwide for Earth Science teaching and communication. As already stressed above, the choice of these Icelandic shallow magma bodies has been suggested by the fact that such geological objects, although extraordinarily challenging both in terms of geotourism and teaching, are seldom the object of attention from the international scientific community. The five VOs are defined here as virtual geosites (VGs) because they are, indeed, geosites that are fully accessible with a smartphone, a tablet, or a PC; moreover, each is provided with a detailed description and notes available during 3D exploration. Our work could represent a model for future, similar efforts aimed at popularizing Earth Sciences and making geoheritage available to a broad public through VGs.
• Padchest: A large chest x-ray image dataset with multi-label annotated reports. Bustos, Aurelia; Pertusa, Antonio; Salinas, Jose-Maria; De La Iglesia-Vaya, Maria (2020). 66 101797.
• Sarcasm Detection Beyond using Lexical Features. Gotham, Mark; Kleinertz, Rainer; Weiß, Christof; Müller, Meinard; Klauk, Stephanie A. J. Oluwaseyi, O. I. David (eds.) (2020). (Vol. 12) 41–53.

  In current time, social media websites such as facebook, twitter, and so forth have improved and received substantial importance. These websites have grown into huge environments wherein users explicit their thoughts, perspectives and reviews evidently. Organizations leverage this environment to tap into people’s opinion on their services and to make a quick feedback. This research seeks to keep away from using grammatical words as the only features for sarcasm detection however also the contextual features, which are theories explaining when, how and why sarcasm is expressed. A deep neural network architecture model was employed to carry out this task, which is a bidirectional long short-term memory with conditional random fields (Bi-LSTM-CRF), two stages were employed to classify if a reply or comment to a tweet is sarcastic or non-sarcastic. The performance of the models was evaluated using the following metrics: Accuracy, Precision, Recall, F-measure.
• 3D digital outcrop model reconstruction of the Kimberley outcrop (Gale crater, Mars) and its integration into Virtual Reality for simulated geological analysis. Caravaca, Gwénaël; Le Mouélic, Stéphane; Mangold, Nicolas; L’Haridon, Jonas; Le Deit, Laetitia; Massé, Marion (2020). 182 104808.

  Structure-from-Motion photogrammetry has recently become a cheap and efficient method to reconstruct accurate and highly-resolved 3D Digital Outcrop Model (DOM) from a single set of images. This enables the 3D visualization of hardly accessible and/or remote geological scenes, which is of strong interest for planetary bodies. This paper focuses on the reconstruction of the DOM of the Kimberley outcrop (Gale crater, Mars) using the Agisoft PhotoScan Professional software. This software is used to compute an accurate, scaled and georeferenced 3D mesh of this outcrop from set of multi-scale images taken by the Mars Science Laboratory rover Curiosity. This model was merged with a 3D model computed from orbital images from the High Resolution Imaging Science Experiment camera (HiRISE) to provide the context. One of the challenges is to integrate data coming from different cameras (with varying optical parameters) not specifically designed for 3D rendering, and with limited points of views. While the obtained DOM allows to observe and characterize geological features of Kimberley’s sedimentary series, classic viewing methods through a 2D screen limits the understanding of the real 3D geometry and scale of the outcrop, as there is no feature such as trees or roads on Mars to provide size references to the user. To overcome this issue and facilitate the interpretation of the DOM, the latter is integrated into a Virtual Reality (VR) environment that enables one or several users working in a collaborative mode to experience a real scale, reliable and realistic depiction of the actual geometries of the geological features reconstructed on the mesh. Precise and accurate description, contextualization of the samplings and mapping of the Kimberley outcrop can therefore be achieved in VR allowing for more precise characterization and interpretations, the same way one would do on a real geological field trip.
• Integrating Virtual Reality and GIS Tools for Geological Mapping, Data Collection and Analysis: An Example from the Metaxa Mine, Santorini (Greece). Antoniou, Varvara; Bonali, Fabio Luca; Nomikou, Paraskevi; Tibaldi, Alessandro; Melissinos, Paraskevas; Mariotto, Federico Pasquaré; Vitello, Fabio Roberto; Krokos, Mel; Whitworth, Malcolm (2020). 10(23)

  In the present work we highlight the effectiveness of integrating different techniques and tools for better surveying, mapping and collecting data in volcanic areas. We use an Immersive Virtual Reality (IVR) approach for data collection, integrated with Geographic Information System (GIS) analysis in a well-known volcanological site in Santorini (Metaxa mine), a site where volcanic processes influenced the island’s industrial development, especially with regard to pumice mining. Specifically, we have focused on: (i) three-dimensional (3D) high-resolution IVR scenario building, based on Structure from Motion photogrammetry (SfM) modeling; (ii) subsequent geological survey, mapping and data collection using IVR; (iii) data analysis, e.g., calculation of extracted volumes, as well as production of new maps in a GIS environment using input data directly from the IVR survey; and finally, (iv) presentation of new outcomes that highlight the importance of the Metaxa Mine as a key geological and volcanological geosite.
• Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation. Majkowska, Anna; Mittal, Sid; Steiner, David F; Reicher, Joshua J; McKinney, Scott Mayer; Duggan, Gavin E; Eswaran, Krish; Cameron Chen, Po-Hsuan; Liu, Yun; Kalidindi, Sreenivasa Raju; others (2020). 294(2) 421–431.
• Automatisierte Textanalyse bei der Personalauswahl – Potenziale und Grenzen. Folkerts, Finn; Riazy, Shirin; Simbeck, Katharina (2019). 48(3) 27–29.

  Unternehmen können heute mithilfe von Algorithmen eine Vorauswahl aus eingegangenen Bewerbungen treffen. Dabei setzen sie auch automatisierte Verfahren der Textanalyse durch künstliche Intelligenz ein. Die dahinterliegenden Routinen sind jedoch nicht nachvollziehbar und können unerwünscht diskriminierend wirken. Im Beitrag werden Ergebnisse eines Projekts vorgestellt, in dem die Voreingenommenheit deutschsprachiger Analysesysteme untersucht wurde.
• An Analysis of Surface and Growth Differences in Plants of Different Stages Using Image Processing. Faizur Rashid, Tamirat Delesa (F. Rashid, ed.) (2019). 9(4) 7.

  Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem growth from birth to maturity level of selected plant using image processing technique. Few reasons have been observed commonly by the researchers that some plants could not grow sufficiently as to the other plants of similar breed and age. Images were taken of different stage of bean plant and images of some other plant samples were also included for better assessment. Researchers are trying to understand through their calculative analysis by image processing emulator in MATLAB to view its reasons.Suitable comparison technique is applied to detect their period of growth. Image processing methods like Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB.Those effects that are not supporting to grow the plants of their similar age group are matter to calculate scientifically later in the future. The observation would help for further support in medicinal science or agriculturalscience of plant and Bio-chemical.
• Performing submarine field survey without scuba gear using GIS-like mapping in a Virtual Reality environment. Billant, Jérémy; Bozzinio, Júlia; Leclerc, Frédérique; Escartin, Javier; Gracias, Nuno; Istenic, Klemen; Arnaubec, Aurelien; Garcia, Rafael (2019). 1–6.
• Conexp-Clj - A Research Tool for FCA. Hanika, Tom; Hirth, Johannes in CEUR Workshop Proceedings, D. Cristea, F. L. Ber, R. Missaoui, L. Kwuida, B. Sertkaya (eds.) (2019). (Vol. 2378) 70–75.
• MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Johnson, Alistair EW; Pollard, Tom J; Berkowitz, Seth J; Greenbaum, Nathaniel R; Lungren, Matthew P; Deng, Chih-ying; Mark, Roger G; Horng, Steven (2019). 6(1) 317.
• Self-Attention with Relative Position Representations. Shaw, Peter; Uszkoreit, Jakob; Vaswani, Ashish (2018). 464–468.
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. Rajpurkar, P (2017). 5225
• Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Wang, Xiaosong; Peng, Yifan; Lu, Le; Lu, Zhiyong; Bagheri, Mohammadhadi; Summers, Ronald M (2017). 2097–2106.
• First direct observation of coseismic slip and seafloor rupture along a submarine normal fault and implications for fault slip history. Escartín, Javier; Leclerc, Frédérique; Olive, Jean-Arthur; Mevel, Catherine; Cannat, Mathilde; Petersen, Sven; Augustin, Nico; Feuillet, Nathalie; Deplus, Christine; Bezos, Antoine; Bonnemains, Diane; Chavagnac, Valérie; Choi, Yujin; Godard, Marguerite; Haaga, Kristian A.; Hamelin, Cédric; Ildefonse, Benoit; Jamieson, John W.; John, Barbara E.; Leleu, Thomas; MacLeod, Christopher J.; Massot-Campos, Miquel; Nomikou, Paraskevi; Paquet, Marine; Rommevaux-Jestin, Céline; Rothenbeck, Marcel; Steinführer, Anja; Tominaga, Masako; Triebe, Lars; Campos, Ricard; Gracias, Nuno; Garcia, Rafael; Andreani, Muriel; Vilaseca, Géraud (2016). 450 96–107.

  Properly assessing the extent and magnitude of fault ruptures associated with large earthquakes is critical for understanding fault behavior and associated hazard. Submarine faults can trigger tsunamis, whose characteristics are defined by the geometry of seafloor displacement, studied primarily through indirect observations (e.g., seismic event parameters, seismic profiles, shipboard bathymetry, coring) rather than direct ones. Using deep-sea vehicles, we identify for the first time a marker of coseismic slip on a submarine fault plane along the Roseau Fault (Lesser Antilles), and measure its vertical displacement of ∼0.9 m in situ. We also map recent fissuring and faulting of sediments on the hangingwall, along ∼3 km of rupture in close proximity to the fault's base, and document the reactivation of erosion and sedimentation within and downslope of the scarp. These deformation structures were caused by the 2004 Mw 6.3 Les Saintes earthquake, which triggered a subsequent tsunami. Their characterization informs estimates of earthquake recurrence on this fault and provides new constraints on the geometry of fault rupture, which is both shorter and displays locally larger coseismic displacements than available model predictions that lack field constraints. This methodology of detailed field observations coupled with near-bottom geophysical surveying can be readily applied to numerous submarine fault systems, and should prove useful in evaluating seismic and tsunamigenic hazard in all geodynamic contexts.
• U-Net: Convolutional Networks for Biomedical Image Segmentation. Ronneberger, Olaf; Fischer, Philipp; Brox, Thomas (2015).
• Fuzzy-Based Multiple Path Selection Method for Improving Energy Efficiency in Bandwidth-Efficient Cooperative Authentications of WSNS. Nam, Su Man; Cho, Tae Ho (2014).
• SIMILARITY MEASURES FOR WEB SERVICE COMPOSITION MODELS. Bravo, Maricela (2014). 5(1) 01–16.

  A Web service composition is an interconnected set of multiple specialized Web service operations, which complement each other to offer an improved tool capable of solving more complex problems. Manual design and implementation of Web service compositions are among the most difficult and error prone tasks. To face this complexity and to reduce errors at design time, the developer can alternatively search and reuse existing compositions that have solved similar problems. Thus the problem of designing and implementing Web service compositions can be reduced to the problem of finding and selecting the composition closest to an initial specification. To achieve this goal, there is the need to define and use similarity measures to determine how close is a given composition with respect to any given specification. Comparison of Web service compositions can be done using two possible sources: composition designs (models), and execution logs of compositions. In particular, in this paper a set of similarity measures are described for Web service composition models. The main objective is to measure and assess the degree of closeness between two given compositions of Web services regardless of their modelling language.
• Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. Candemir, Sema; Jaeger, Stefan; Palaniappan, Kannappan; Musco, Jonathan P; Singh, Rahul K; Xue, Zhiyun; Karargyris, Alexandros; Antani, Sameer; Thoma, George; McDonald, Clement J (2013). 33(2) 577–590.
• Describing Textures in the Wild. Cimpoi, Mircea; Maji, Subhransu; Kokkinos, Iasonas; Mohamed, Sammy; Vedaldi, Andrea (2013).
• The Puncher & Wattmann anthology of Australian poetry Leonard, John (2009). Puncher & Wattmann, Glebe, N.S.W.
• Statistics. An Introduction using R Crawley, Michael J. (2005). Wiley, Chichester, West Sussex.
• Fuzzy Engineering Kosko, Bart (93C42, ed.) (1997). Hemel Hempstead: Prentice Hall.
• Formale Begriffsanalyse Ganter, Bernhard; Wille, Rudolf (1996). Springer, Berlin, Heidelberg.

  Dieses erste Lehrbuch zur Formalen Begriffsanalyse gibt eine systematische Darstellung der mathematischen Grundlagen und ihrer Verbindung zu Anwendungen in der Informatik, insbesondere in der Datenanalyse und Wissensverarbeitung. Das Buch vermittelt vor allem Methoden der graphischen Darstellung von Begriffssystemen, die sich in der Wissenskommunikation bestens bewährt haben. Theorie und graphische Darstellung werden dabei eng miteinander verknüpft. Die mathematischen Grundlagen werden vollständig abgehandelt und durch zahlreiche Beispiele anschaulich gemacht. Da zur Wissensverarbeitung immer stärker der Computer genutzt wird, gewinnen formale Methoden begrifflicher Analyse überall an Bedeutung. Das Buch macht die dafür grundlegende Theorie in kompakter Form zugänglich.
• Fuzzy Thinking : The New Science of Fuzzy Logic Kosko, Bart (1993). Flamingo, London.
• Introduction to systems analysis and design Hawryszkiewycz, I. T. (1988). Prentice Hall, New York.
• Seven Poor Men of Sydney Stead, Christina in Australian Classics (1978). Angus & Robertson, London.
• The puzzleheaded girl : four novellas. Christina Stead Stead, Christina (1967). Holt, Rinehart and Winston, New York.