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论“数字化大学”的内涵及发展
(2022)
Zum Geleit
(2022)
Wie KI Innere Führung lernt
(2022)
Dass sich künstliche Intelligenz (KI) weltweit ausgebreitet hat, ist eine Binsenwahrheit. Die rasche und unaufhaltsame Proliferation von KI der letzten zehn Jahre spricht für sich, und längst ziehen auch Gesetzgeber und Regulierungsbehörden nach, um KI und ihre Technikfolgen einzuhegen. Für Deutschland relevante Gestaltungsanforderungen haben die High-Level Expert Group on Artificial Intelligence der Europäischen Kommission (HLEG AI) und auf nationaler Ebene die Datenethikkommission der Bundesregierung (DEK) und die Enquetekommission Künstliche Intelligenz des Deutschen Bundestags (EKKI) geäußert.
Wehren erlaubt
(2022)
Seit Sokrates bildet die Frage „Was macht ein glückliches Leben aus?“ den Ausgangspunkt der Entwicklung einer Vielfalt von Wohlbefindenstheorien. Den Kern dieses Aufsatzes bildet die Erörterung der Fragen, inwieweit das Konzept der empirischen Lebenszufriedenheit und die dadurch gewonnenen Korrelate einen Beitrag zur Beantwortung dieser Frage leisten und ob diese Antworten eine Wohlbefindenstheorie begründen können, welche die philosophische Theorie mit empirischen Ergebnissen verknüpft.
Im Zentrum dieses Aufsatzes steht eine Diskussion der wichtigsten Wohlbefindenstheorien, ihrer Qualitäten, Gemeinsamkeiten und Unterschiede. Einen Schwerpunkt bildet die Theorie der subjektiven Lebenszufriedenheit. Ich diskutiere Stärken und Schwächen des Konzeptes und stelle die wichtigsten Ergebnisse der empirischen Lebenszufriedenheitsforschung in einem Überblick dar.
Im Ergebnis argumentiere ich, dass die Resultate der empirischen Forschung als Grundlage einer subjektiv-objektiven Wohlbefindenstheorie dienen können. Qualitativ hochwertige zwischenmenschliche Beziehungen, ein gesunder Lebensstil, eine ausgewogene Work-Life-Balance, der Einsatz für Andere, das Verfolgen von Lebenszielen und persönlichen Interessen bilden die Grundlage einer Wohlbefindenstheorie, die sich auf empirische Lebenszufriedenheitsforschung stützt.
Was ist ein Labor?
(2022)
Der technische Fortschritt im Bereich der Erhebung, Speicherung und Verarbeitung von Daten macht es erforderlich, neue Fragen zu sozialverträglichen Datenmärkten aufzuwerfen. So gibt es sowohl eine Tendenz zur vereinfachten Datenteilung als auch die Forderung, die informationelle Selbstbestimmung besser zu schützen. Innerhalb dieses Spannungsfeldes bewegt sich die Idee von Datentreuhändern. Ziel des Beitrags ist darzulegen, dass zwischen verschiedenen Formen der Datentreuhänderschaft unterschieden werden sollte, um der Komplexität des Themas gerecht zu werden. Insbesondere bedarf es neben der mehrseitigen Treuhänderschaft, mit dem Treuhänder als neutraler Instanz, auch der einseitigen Treuhänderschaft, bei dem der Treuhänder als Anwalt der Verbraucherinteressen fungiert. Aus dieser Perspektive wird das Modell der Datentreuhänderschaft als stellvertretende Deutung der Interessen individueller und kollektiver Identitäten systematisch entwickelt.
Vorwort
(2022)
Vorwort
(2022)
Personal-Information-Management-Systeme (PIMS) gelten als Chance, um die Datensouveränität der Verbraucher zu stärken. Datenschutzbezogene Fragen sind für Verbraucher immer dort relevant, wo sie Verträge und Nutzungsbedingungen mit Diensteanbietern eingehen. Vor diesem Hintergrund diskutiert dieser Beitrag die Potenziale von VRM-Systemen, die nicht nur das Datenmanagement, sondern das gesamte Vertragsmanagement von Verbrauchern unterstützen. Dabei gehen wir der Frage nach, ob diese besser geeignet sind, um Verbraucher zu souveränem Handeln zu befähigen.
Virtual exchange
(2022)
Vection underwater
(2022)
Unlimited paid time off policies are currently fashionable and widely discussed by HR professionals around the globe. While on the one hand, paid time off is considered a key benefit by employees and unlimited paid time off policies (UPTO) are seen as a major perk which may help in recruiting and retaining talented employees, on the other hand, early adopters reported that employees took less time off than previously, presumably leading to higher burnout rates. In this conceptual review, we discuss the theoretical and empirical evidence regarding the potential effects of UPTO on leave utilization, well-being and performance outcomes. We start out by defining UPTO and placing it in a historical and international perspective. Next, we discuss the key role of leave utilization in translating UPTO into concrete actions. The core of our article constitutes the description of the effects of UPTO and the two pathways through which these effects are assumed to unfold: autonomy need satisfaction and detrimental social processes. We moreover discuss the boundary conditions which facilitate or inhibit the successful utilization of UPTO on individual, team, and organizational level. In reviewing the literature from different fields and integrating existing theories, we arrive at a conceptual model and five propositions, which can guide future research on UPTO. We conclude with a discussion of the theoretical and societal implications of UPTO.
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series
(2022)
Deep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the outcome trustworthiness. In order to have a better understanding of the behavior of a system, particularly one driven by time series, a look inside a deep learning model so-called posthoc eXplainable Artificial Intelligence (XAI) approaches, is important. There are two major types of XAI for time series data, namely model-agnostic and model-specific. Model-specific approach is considered in this work. While other approaches employ either Class Activation Mapping (CAM) or Attention Mechanism, we merge the two strategies into a single system, simply called the Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series (TSEM). TSEM combines the capabilities of RNN and CNN models in such a way that RNN hidden units are employed as attention weights for the CNN feature maps temporal axis. The result shows that TSEM outperforms XCM. It is similar to STAM in terms of accuracy, while also satisfying a number of interpretability criteria, including causality, fidelity, and spatiotemporality.
Microarray-based experiments revealed that thyroid hormone triiodothyronine (T3) enhanced the binding of Cy5-labeled ATP on heat shock protein 90 (Hsp90). By molecular docking experiments with T3 on Hsp90, we identified a T3 binding site (TBS) near the ATP binding site on Hsp90. A synthetic peptide encoding HHHHHHRIKEIVKKHSQFIGYPITLFVEKE derived from the TBS on Hsp90 showed, in MST experiments, the binding of T3 at an EC50 of 50 μM. The binding motif can influence the activity of Hsp90 by hindering ATP accessibility or the release of ADP.
It is challenging to provide users with a haptic weight sensation of virtual objects in VR since current consumer VR controllers and software-based approaches such as pseudo-haptics cannot render appropriate haptic stimuli. To overcome these limitations, we developed a haptic VR controller named Triggermuscle that adjusts its trigger resistance according to the weight of a virtual object. Therefore, users need to adapt their index finger force to grab objects of different virtual weights. Dynamic and continuous adjustment is enabled by a spring mechanism inside the casing of an HTC Vive controller. In two user studies, we explored the effect on weight perception and found large differences between participants for sensing change in trigger resistance and thus for discriminating virtual weights. The variations were easily distinguished and associated with weight by some participants while others did not notice them at all. We discuss possible limitations, confounding factors, how to overcome them in future research and the pros and cons of this novel technology.
Regions and their innovation ecosystems have increasingly become of interest to CSCW research as the context in which work, research and design takes place. Our study adds to this growing discourse, by providing preliminary data and reflections from an ongoing attempt to intervene and support a regional innovation ecosystem. We report on the benefits and shortcomings of a practice-oriented approach in such regional projects and highlight the importance of relations and the notion of spillover. Lastly, we discuss methodological and pragmatic hurdles that CSCW research needs to overcome in order to support regional innovation ecosystems successfully.
Trojanized software packages used in software supply chain attacks constitute an emerging threat. Unfortunately, there is still a lack of scalable approaches that allow automated and timely detection of malicious software packages and thus most detections are based on manual labor and expertise. However, it has been observed that most attack campaigns comprise multiple packages that share the same or similar malicious code. We leverage that fact to automatically reproduce manually identified clusters of known malicious packages that have been used in real world attacks, thus, reducing the need for expert knowledge and manual inspection. Our approach, AST Clustering using MCL to mimic Expertise (ACME), yields promising results with a 𝐹1 score of 0.99. Signatures are automatically generated based on characteristic code fragments from clusters and are subsequently used to scan the whole npm registry for unreported malicious packages. We are able to identify and report six malicious packages that have been removed from npm consequentially. Therefore, our approach can support the detection by reducing manual labor and hence may be employed by maintainers of package repositories to detect possible software supply chain attacks through trojanized software packages.
Thermo-chemical conversion of cucumber peel waste for biobased energy and chemical production
(2022)
Therapeutic Treatments for Osteoporosis-Which Combination of Pills Is the Best among the Bad?
(2022)
Osteoporosis is a chronical, systemic skeletal disorder characterized by an increase in bone resorption, which leads to reduced bone density. The reduction in bone mineral density and therefore low bone mass results in an increased risk of fractures. Osteoporosis is caused by an imbalance in the normally strictly regulated bone homeostasis. This imbalance is caused by overactive bone-resorbing osteoclasts, while bone-synthesizing osteoblasts do not compensate for this. In this review, the mechanism is presented, underlined by in vitro and animal models to investigate this imbalance as well as the current status of clinical trials. Furthermore, new therapeutic strategies for osteoporosis are presented, such as anabolic treatments and catabolic treatments and treatments using biomaterials and biomolecules. Another focus is on new combination therapies with multiple drugs which are currently considered more beneficial for the treatment of osteoporosis than monotherapies. Taken together, this review starts with an overview and ends with the newest approaches for osteoporosis therapies and a future perspective not presented so far.
The corporate landscape is experiencing an increasing change in business models due to digitization. An increasing availability of data along the business processes enhance the opportunities for process automation. Technologies such as Robotic Process Automation (RPA) are widely used for business process optimization, but as a side effect an increase in stand-alone solutions and a lack of holistic approaches can be observed. Intelligent Process Automation (IPA) is said to support more complex processes and enable automated decision-making, but due to the lack of connectors makes the implementation difficult. RPA marketplaces can be a bridging technology to help companies implement Intelligent Process Automation. This paper explores the drivers and challenges for the adoption of RPA marketplaces to realize IPA. For this purpose, we conducted ten expert interviews with decision makers and IT staff from the process automation sector.
This edited volume on “Recent Advances in Renewable Energy” presents a selection of refereed papers presented at the 1st International Conference on Electrical Systems and Automation. The book provides rigorous discussions, the state of the art, and recent developments in the field of renewable energy sources supported by examples and case studies, making it an educational tool for relevant undergraduate and graduate courses. The book will be a valuable reference for beginners, researchers, and professionals interested in renewable energy.
This book which is the second part of two volumes on ''Control of Electrical and Electronic Systems” presents a compilation of selected contributions to the 1st International Conference on Electrical Systems & Automation. The book provides rigorous discussions, the state of the art, and recent developments in the modelling, simulation and control of power electronics, industrial systems, and embedded systems. The book will be a valuable reference for beginners, researchers, and professionals interested in control of electrical and electronic systems.
The Poverty Reduction Effect of Social Protection: The Pros and Cons of a Multidisciplinary Approach
(2022)
There is a growing body of knowledge on the complex effects of social protection on poverty in Africa. This article explores the pros and cons of a multidisciplinary approach to studying social protection policies. Our research aimed at studying the interaction between cash transfers and social health protection policies in terms of their impact on inclusive growth in Ghana and Kenya. Also, it explored the policy reform context over time to unravel programme dynamics and outcomes. The analysis combined econometric and qualitative impact assessments with national- and local-level political economic analyses. In particular, dynamic effects and improved understanding of processes are well captured by this approach, thus, pushing the understanding of implementation challenges over and beyond a ‘technological fix,’ as has been argued before by Niño-Zarazúa et al. (World Dev 40:163–176, 2012), However, multidisciplinary research puts considerable demands on data and data handling. Finally, some poverty reduction effects play out over a longer time, requiring longitudinal consistent data that is still scarce.
Background: Since presenteeism is related to numerous negative health and work-related effects, measures are required to reduce it. There are initial indications that how an organization deals with health has a decisive influence on employees’ presenteeism behavior.
Aims: The concept of health-promoting collaboration was developed on the basis of these indications. As an extension of healthy leadership it includes not only the leader but also co-workers. In modern forms of collaboration, leaders cannot be assigned sole responsibility for employees’ health, since the leader is often hardly visible (digital leadership) or there is no longer a clear leader (shared leadership). The study examines the concept of health-promoting collaboration in relation to presenteeism. Relationships between health-promoting collaboration, well-being and work ability are also in focus, regarding presenteeism as a mediator.
Methods: The data comprise the findings of a quantitative survey of 308 employees at a German university of applied sciences. Correlation and mediator analyses were conducted.
Results: The results show a significant negative relationship between health-promoting collaboration and presenteeism. Significant positive relationships were found between health-promoting collaboration and both well-being and work ability. Presenteeism was identified as a mediator of these relationships.
Conclusion: The relevance of health-promoting collaboration in reducing presenteeism was demonstrated and various starting points for practice were proposed. Future studies should investigate further this newly developed concept in relation to presenteeism.
The Chemotype of Chromanones as a Privileged Scaffold for Multineurotarget Anti-Alzheimer Agents
(2022)
Integrated solar water splitting devices that produce hydrogen without the use of power inverters operate outdoors and are hence exposed to varying weather conditions. As a result, they might sometimes work at non-optimal operation points below or above the maximum power point of the photovoltaic component, which would directly translate into efficiency losses. Up until now, however, no common parameter describing and quantifying this and other real-life operating related losses (e.g. spectral mismatch) exists in the community. Therefore, the annual-hydrogen-yield-climatic-response (AHYCR) ratio is introduced as a figure of merit to evaluate the outdoor performance of integrated solar water splitting devices. This value is defined as the ratio between the real annual hydrogen yield and the theoretical yield assuming the solar-to-hydrogen device efficiency at standard conditions. This parameter is derived for an exemplary system based on state-of-the-art AlGaAs//Si dual-junction solar cells and an anion exchange membrane electrolyzer using hourly resolved climate data from a location in southern California and from reanalysis data of Antarctica. This work will help to evaluate, compare and optimize the climatic response of solar water splitting devices in different climate zones.
Die Digitalisierung und der Einsatz von Informations- und Kommunikationstechnologien (ICT) hat im Arbeits- und Privatleben neben einer höheren Produktivität auch zu neuen Formen von psychischem Stress geführt. Das Stresserleben, das mit dem Einsatz von ICT verbunden ist, wird in der Literatur auch als Technostress bezeichnet. Die Forschung zu diesem Thema zeigt, dass die Entstehung von Technostress von individuellen Faktoren abhängt. Die Persönlichkeit von ICT-Anwenderinnen und Anwendern bestimmt nicht nur das Auftreten von Technostress, sondern hat auch Einfluss auf dessen gesundheitliche und leistungsbezogene Folgen. In diesem Literaturreview wird der Forschungsstand zu der Rolle von Persönlichkeitsunterschieden bei der Entstehung von Technostress und dessen Folgen systematisch zusammengefasst. Die Auswertung der relevanten Forschungsartikel erfolgt hinsichtlich verwendeter Variablen, Stichproben und Studiendesigns, statistischer Methoden, Theorien und Frameworks. Abschließend werden der aktuelle Forschungsstand eingeordnet und Forschungslücken aufgezeigt.
Technik-Ästhetik
(2022)
Von Technik und Ästhetik zu sprechen, heißt, sich bereits begrifflich auf ein Feld einzulassen, das mindestens zwei divergente Perspektiven gemeinsam denkt. Dabei haben das Technische wie das Ästhetische die Bedeutung einer Interdependenz aufzuweisen: Das Technische konstituiert einerseits Funktionen, Formen und Gebrauchsaspekte - ästhetische Zustände evozieren andererseits zeichenhafte Realisierungen, phantasmatische Urteile und wahrnehmungsvermittelte Phänomene des Erscheinens. Die Beiträger*innen des Bandes zeigen, wie bei der Konfrontation von Technik und Ästhetik eine Art Verkopplung und intrinsische Dynamik qua techno-ästhetischer Evokation entstehen kann.
This paper investigates the effect of voltage sensors on the measurement of transient voltages for power semiconductors in a Double Pulse Test (DPT) environment.We adapt previously published models that were developed for current sensors and apply them to voltage sensors to evaluate their suitability for DPT applications. Similarities and differences between transient current and voltage sensors are investigated and the resulting methodology is applied to commercially available and experimental voltage sensors. Finally, a selection aid for given measurement tasks is derived that focuses on the measurement of fast-switching power semiconductors.
Flüssigkeit, die in Werbespots symbolisch für Menstruationsblut steht, war jahrzehntelang blau, erst im September 2021 zeigte ein Hersteller erstmalig eine Flüssigkeit, welche realitätsnah in der Farbe Rot dargestellt wurde (1). Hygieneartikel, die Menstruierende zwingend benötigen, sind in Deutschland mit wenigen Ausnahmen auf öffentlichen Toiletten nicht verfügbar: Das Nicht-Sichtbarsein offenbarte auch im Jahr 2021 das Tabu um natürliche biologische Prozesse des weiblichen Körpers. Scham und Einschränkungen, die sich verhindern ließen, sind die Folge. Menstruierende werden in ihrem Wohlbefinden limitiert, und negative Erlebnisse führen dazu, dass Betroffene in der Ausübung von sozialen, schulischen und beruflichen Aktivitäten nicht nur durch die Menstruation selbst, sondern auch durch Normen und Erziehungsmuster beeinträchtigt sind, wie zahlreiche internationale Studien gezeigt haben (2). Für den deutschen Hochschulkontext fehlen solche Studien bislang.
In young adulthood, important foundations are laid for health later in life. Hence, more attention should be paid to the health measures concerning students. A research field that is relevant to health but hitherto somewhat neglected in the student context is the phenomenon of presenteeism. Presenteeism refers to working despite illness and is associated with negative health and work-related effects. The study attempts to bridge the research gap regarding students and examines the effects of and reasons for this behavior. The consequences of digital learning on presenteeism behavior are moreover considered. A student survey (N = 1036) and qualitative interviews (N = 11) were conducted. The results of the quantitative study show significant negative relationships between presenteeism and health status, well-being, and ability to study. An increased experience of stress and a low level of detachment as characteristics of digital learning also show significant relationships with presenteeism. The qualitative interviews highlighted the aspect of not wanting to miss anything as the most important reason for presenteeism. The results provide useful insights for developing countermeasures to be easily integrated into university life, such as establishing fixed learning partners or the use of additional digital learning material.
MOTIVATION
The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models (KGEMs). However, representations based on a single modality are inherently limited.
RESULTS
To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs (KGs). This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations in a shared embedding space. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler (INDRA) consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against three baseline models trained on either one of the modalities (i.e., text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.084 (i.e., from 0.881 to 0.965). Finally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications.
AVAILABILITY
We make the source code and the Python package of STonKGs available at GitHub (https://github.com/stonkgs/stonkgs) and PyPI (https://pypi.org/project/stonkgs/). The pre-trained STonKGs models and the task-specific classification models are respectively available at https://huggingface.co/stonkgs/stonkgs-150k and https://zenodo.org/communities/stonkgs.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Statistik im Fokus
(2022)
This 2nd edition compendium contains and explains essential statistical formulas within an economic context. Expanded by more than 100 pages compared to the 1st edition, the compendium has been supplemented with numerous additional practical examples, which will help readers to better understand the formulas and their practical applications. This statistical formulary is presented in a practice-oriented, clear, and understandable manner, as it is needed for meaningful and relevant application in global business, as well as in the academic setting and economic practice. (Verlagsangaben)
In this paper, modeling of piston and generic type gas compressors for a globally convergent algorithm for solving stationary gas transport problems is carried out. A theoretical analysis of the simulation stability, its practical implementation and verification of convergence on a realistic gas network have been carried out. The relevance of the paper for the topics of the conference is defined by a significance of gas transport networks as an advanced application of simulation and modeling, including the development of novel mathematical and numerical algorithms and methods.
Using a life-cycle approach, we identify key gaps for social reform in Georgia. The reduction of informal work is the most pressing of these, since formal employment is the backbone of any robust and reliable social insurance scheme. At the same time, greater financial resources are required through taxation in order to enable systematic social reform in Georgia. Both interventions are needed in order to fill the gaps in the current social protection system, which include the limited scope of pension and health insurance, as well as the lack of permanent unemployment insurance and universal child benefits.
Against the background of Germany’s long experience with social protection, we outline the main principles of the German welfare state and present the design of three main social insurance branches (pensions, health and unemployment). Based on the mixed experience that has emerged in Germany, in particular due to path dependencies and political deadlock, we derive lessons that inform a clear and coherent vision for social reform in Georgia.
The utilization of simulation procedures is gaining increasing attention in the product development of extrusion blow molded parts. However, some simulation steps, like the simulation of shrinkage and warpage, are still associated with uncertainties. The reason for this is on the one hand a lack of standardized interfaces for the transfer of simulation data between different simulation tools, and on the other hand the complex time-, temperature- and process-dependent material behavior of the used semi crystalline polymers. Using a new vendor neutral interface standard for the data transfer, the shrinkage analysis of a simple blow molded part is investigated and compared to experimental data. A linear viscoelastic material model in combination with an orthotropic process- and temperature-dependent thermal expansion coefficient is used for the shrinkage prediction. A good agreement is observed. Finally, critical parameters in the simulation models that strongly influence the shrinkage analysis are identified by a sensitivity study.
A precise characterization of substances is essential for the safe handling of explosives. One parameter regularly characterized is the impact sensitivity. This is typically determined using a drop hammer. However, the results can vary depending on the test method and even the operator, and it is not possible to distinguish the type of decomposition such as detonation and deflagration. This study monitors the reaction progress by constructing a drop hammer to measure the decomposition reaction of four different primary explosives (tetrazene, silver azide, lead azide, lead styphnate) in order to determine the reproducibility of this method. Additionally, further possible evaluation methods are explored to improve on the current binary statistical analysis. To determine whether classification was possible based on extracted features, the responses of equipped sensor arrays, which measure and monitor the reactions, were studied and evaluated. Features were extracted from this data and were evaluated using multivariate methods such as principal component analysis (PCA) and linear discriminant analysis (LDA). The results indicate that although the measurements show substance specific trends, they also show a large scatter for each substance. By reducing the dimensions of the extracted features, different sample clusters can be represented and the calculated loadings allow significant parameters to be determined for classification. The results also suggest that differentiation of different reaction mechanisms is feasible. Testing of the regressor function shows reliable results considering the comparatively small amount of data.
Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception capabilities such as sonar image classification. Due to the confidential nature of sonar imaging and the difficulty to interpret sonar images, it is challenging to create public large labeled sonar datasets to train supervised learning algorithms. In this work, we investigate the potential of three self-supervised learning methods (RotNet, Denoising Autoencoders, and Jigsaw) to learn high-quality sonar image representation without the need of human labels. We present pre-training and transfer learning results on real-life sonar image datasets. Our results indicate that self-supervised pre-training yields classification performance comparable to supervised pre-training in a few-shot transfer learning setup across all three methods. Code and self-supervised pre-trained models are be available at https://github.com/agrija9/ssl-sonar-images
In recent years, the ability of intelligent systems to be understood by developers and users has received growing attention. This holds in particular for social robots, which are supposed to act autonomously in the vicinity of human users and are known to raise peculiar, often unrealistic attributions and expectations. However, explainable models that, on the one hand, allow a robot to generate lively and autonomous behavior and, on the other, enable it to provide human-compatible explanations for this behavior are missing. In order to develop such a self-explaining autonomous social robot, we have equipped a robot with own needs that autonomously trigger intentions and proactive behavior, and form the basis for understandable self-explanations. Previous research has shown that undesirable robot behavior is rated more positively after receiving an explanation. We thus aim to equip a social robot with the capability to automatically generate verbal explanations of its own behavior, by tracing its internal decision-making routes. The goal is to generate social robot behavior in a way that is generally interpretable, and therefore explainable on a socio-behavioral level increasing users' understanding of the robot's behavior. In this article, we present a social robot interaction architecture, designed to autonomously generate social behavior and self-explanations. We set out requirements for explainable behavior generation architectures and propose a socio-interactive framework for behavior explanations in social human-robot interactions that enables explaining and elaborating according to users' needs for explanation that emerge within an interaction. Consequently, we introduce an interactive explanation dialog flow concept that incorporates empirically validated explanation types. These concepts are realized within the interaction architecture of a social robot, and integrated with its dialog processing modules. We present the components of this interaction architecture and explain their integration to autonomously generate social behaviors as well as verbal self-explanations. Lastly, we report results from a qualitative evaluation of a working prototype in a laboratory setting, showing that (1) the robot is able to autonomously generate naturalistic social behavior, and (2) the robot is able to verbally self-explain its behavior to the user in line with users' requests.
The non-scientific questioning of scientific research during the COVID-19 pandemic, the unwillingness of a president of the United States of America to accept the result of a democratically held election: just in recent times, there have been quite a few striking examples of long-held certainties appearing as nothing more than just illusions. This essay reflects on the severe consequences of the loss of such certainties in the spheres of democratic politics on the one hand and of science, especially for highly differentiated societies, on the other hand as well as on their interdependencies. Furthermore, the author tries to make the case that this disillusionment could prove to be a salutary shock – reminding us that we need to take a stand for the things we hold as certainties, oftentimes even as calming ones, if we want them to stay how we always thought they were.
Technical aspects are brought into focus thinking of inclusion opportunities and exclusion risks in digital learning scenarios. However, focussing on technical limitations is not sufficient. This contribution describes another important field of inclusion, namely psychological personality traits. In a longitudinal study at the Hochschule Bonn-Rhein-Sieg (H-BRS), University of Applied Sciences, we accompanied a civil law lecture of a bachelor's degree programme, which had been digitalized because of COVID-19, with empirical Scholarship of Teaching and Learning methods for two semesters. N=55 students from the first measured semester and N=35 from the second one rated different digital teaching methods used in the developed digital learning scenario. Their personality traits according to the five-factor model were measured by using a validated psychometric short-scale (BFI-10). Moderate to large empirical effects of the students' personality traits on the assessments of different digital teaching methods, used in the digital learning scenario, could be observed. Neuroticism values influences the perceptions of the course difficulty and the preference for using an instant messenger as a central communication platform, where students can interact with fellows and lecturers in a way the students are used to in their daily life. High conscientiousness predicts a more regular execution of the weekly tasks given throughout the semester, while higher values in extraversion are associated with a preference for synchronous video conference sessions and active webcams. Higher agreeableness is associated with rating the learning atmosphere as more constructive while low values are associated with perceiving more negative consequences due to the reduced contact to fellows based on COVID-19 restrictions. Correlations between the dimension openness and any ratings of digital teaching methods could not be observed. With this insight into our students' personality traits, we were able to match the digital teaching methods used in our digital learning scenario to the psychological needs of our students, which resulted in a higher inclusion level and a reduction of exclusion risks.
Vietnam requires a sustainable urbanization, for which city sensing is used in planning and de-cision-making. Large cities need portable, scalable, and inexpensive digital technology for this purpose. End-to-end air quality monitoring companies such as AirVisual and Plume Air have shown their reliability with portable devices outfitted with superior air sensors. They are pricey, yet homeowners use them to get local air data without evaluating the causal effect. Our air quality inspection system is scalable, reasonably priced, and flexible. Minicomputer of the sys-tem remotely monitors PMS7003 and BME280 sensor data through a microcontroller processor. The 5-megapixel camera module enables researchers to infer the causal relationship between traffic intensity and dust concentration. The design enables inexpensive, commercial-grade hardware, with Azure Blob storing air pollution data and surrounding-area imagery and pre-venting the system from physically expanding. In addition, by including an air channel that re-plenishes and distributes temperature, the design improves ventilation and safeguards electrical components. The gadget allows for the analysis of the correlation between traffic and air quali-ty data, which might aid in the establishment of sustainable urban development plans and poli-cies.
Despite the increasing interest in single family offices (SFOs) as an investment owned by an entrepreneurial family, research on SFOs is still in its infancy. In particular, little is known about the capital structures of SFOs or the roots of SFO heterogeneity regarding financial decisions. By drawing on a hand-collected sample of 104 SFOs and private equity (PE) firms, we compare the financing choices of these two investor types in the context of direct entrepreneurial investments (DEIs). Our data thereby provide empirical evidence that SFOs are less likely to raise debt than PE firms, suggesting that SFOs follow pecking-order theory. Regarding the heterogeneity of the financial decisions of SFOs, our data indicate that the relationship between SFOs and debt financing is reinforced by the idiosyncrasies of entrepreneurial families, such as higher levels of owner management and a higher firm age. Surprisingly, our data do not support a moderating effect for the emphasis placed on socioemotional wealth (SEW).
Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiments.
Robust Identification and Segmentation of the Outer Skin Layers in Volumetric Fingerprint Data
(2022)
Despite the long history of fingerprint biometrics and its use to authenticate individuals, there are still some unsolved challenges with fingerprint acquisition and presentation attack detection (PAD). Currently available commercial fingerprint capture devices struggle with non-ideal skin conditions, including soft skin in infants. They are also susceptible to presentation attacks, which limits their applicability in unsupervised scenarios such as border control. Optical coherence tomography (OCT) could be a promising solution to these problems. In this work, we propose a digital signal processing chain for segmenting two complementary fingerprints from the same OCT fingertip scan: One fingerprint is captured as usual from the epidermis (“outer fingerprint”), whereas the other is taken from inside the skin, at the junction between the epidermis and the underlying dermis (“inner fingerprint”). The resulting 3D fingerprints are then converted to a conventional 2D grayscale representation from which minutiae points can be extracted using existing methods. Our approach is device-independent and has been proven to work with two different time domain OCT scanners. Using efficient GPGPU computing, it took less than a second to process an entire gigabyte of OCT data. To validate the results, we captured OCT fingerprints of 130 individual fingers and compared them with conventional 2D fingerprints of the same fingers. We found that both the outer and inner OCT fingerprints were backward compatible with conventional 2D fingerprints, with the inner fingerprint generally being less damaged and, therefore, more reliable.
Characterization methods of pressure sensitive adhesives (PSA) originate from technical bonding and do not cover relevant data for the development and quality assurance of medical applications, where PSA with flexible backing layers are adopted to human skin. In this study, a new method called RheoTack is developed to determine (mechanically and optically) an adhesion and detaching behavior of flexible and transparent PSA based patches. Transdermal therapeutic systems (TTS) consisting of silicone-based PSAs on a flexible and transparent backing layer were tested on a rotational rheometer with an 8 mm plate as a probe rod at retraction speeds of 0.01, 0.1, and 1 mm/s with respect to their adhesion and detaching behavior in terms of force-retraction displacement curves. The curves consist of a compression phase to affirm wetting; a tensile deformation phase intercepting stretching, cavity, and fibril formation; and a failure phase with detaching. Their analysis provides values for stiffness, force, and displacement of the beginning of fibril formation, force and displacement of the beginning of a failure due to fibril breakage and detaching, as well as corresponding activation energies. All these parameters exhibit the pronounced dependency on the retraction speed. The force-retraction displacement curves together with the simultaneous video recordings of the TTS deformation from three different angles (three cameras) provide deeper insight into the deformation processes and allow for interpreting the properties’ characteristics for PSA applications.
SLC6A14 (ATB0,+) is unique among SLC proteins in its ability to transport 18 of the 20 proteinogenic (dipolar and cationic) amino acids and naturally occurring and synthetic analogues (including anti-viral prodrugs and nitric oxide synthase (NOS) inhibitors). SLC6A14 mediates amino acid uptake in multiple cell types where increased expression is associated with pathophysiological conditions including some cancers. Here, we investigated how a key position within the core LeuT-fold structure of SLC6A14 influences substrate specificity. Homology modelling and sequence analysis identified the transmembrane domain 3 residue V128 as equivalent to a position known to influence substrate specificity in distantly related SLC36 and SLC38 amino acid transporters. SLC6A14, with and without V128 mutations, was heterologously expressed and function determined by radiotracer solute uptake and electrophysiological measurement of transporter-associated current. Substituting the amino acid residue occupying the SLC6A14 128 position modified the binding pocket environment and selectively disrupted transport of cationic (but not dipolar) amino acids and related NOS inhibitors. By understanding the molecular basis of amino acid transporter substrate specificity we can improve knowledge of how this multi-functional transporter can be targeted and how the LeuT-fold facilitates such diversity in function among the SLC6 family and other SLC amino acid transporters.
The cooperation between researchers and practitioners during the different stages of the research process is promoted as it can be of benefit to both society and research supporting processes of ‘transformation’. While acknowledging the important potential of research–practice–collaborations (RPCs), this paper reflects on RPCs from a political-economic perspective to also address potential unintended adverse effects on knowledge generation due to divergent interests, incomplete information or the unequal distribution of resources. Asymmetries between actors may induce distorted and biased knowledge and even help produce or exacerbate existing inequalities. Potential merits and limitations of RPCs, therefore, need to be gauged. Taking RPCs seriously requires paying attention to these possible tensions—both in general and with respect to international development research, in particular: On the one hand, there are attempts to contribute to societal change and ethical concerns of equity at the heart of international development research, and on the other hand, there is the relative risk of encountering asymmetries more likely.
In her recent article, Bender discusses several aspects of research–practice–collaborations (RPCs). In this commentary, we apply Bender's arguments to experiences in engineering research and development (R&D). We investigate the influence of interaction with practice partners on relevance, credibility, and legitimacy in the special engineering field of product development and analyze which methodological approaches are already being pursued for dealing with diverging interests and asymmetries and which steps will be necessary to include interests of civil society beyond traditional customer relations.
Research-Practice-Collaborations Addressing One Health and Urban Transformation. A Case Study
(2022)
One Health is an integrative approach at the interface of humans, animals and the environment, which can be implemented as Research-Practice-Collaboration (RPC) for its interdisciplinarity and intersectoral focus on the co-production of knowledge. To exemplify this, the present commentary shows the example of the Forschungskolleg “One Health and Urban Transformation” funded by the Ministry of Culture and Science of the State Government of Nord Rhine Westphalia in Germany. After analysis, the factors identified for a better implementation of RPC for One Health were the ones that allowed for constant communication and the reduction of power asymmetries between practitioners and academics in the co-production of knowledge. In this light, the training of a new generation of scientists at the boundaries of different disciplines that have mediation skills between academia and practice is an important contribution with great implications for societal change that can aid the further development of RPC.
Soil nutrient depletion threatens global food security and has been seriously underestimated for potassium (K) and several micronutrients. This is particularly the case for highly weathered soils in tropical countries, where classical soluble fertilizers are often not affordable or not accessible. One way to replenish macro- and micronutrients are ground silicate rock powders (SRPs). Rock forming silicate minerals contain most nutrients essential for higher plants, yet slow and inconsistent weathering rates have restricted their use in the past. Recent findings, however, challenge past agronomic objections which insufficiently addressed the factorial complexity of the weathering process. This review therefore first presents a framework with the most relevant factors for the weathering of SRPs through which several outcomes of prior studies can be explained. A subsequent analysis of 48 crop trials reveals the potential as alternative K source and multi-nutrient soil amendment for tropical soils, whereas the benefits for temperate soils are currently inconclusive. Beneficial results prevail for mafic and ultramafic rocks like basalts and rocks containing nepheline or glauconite. Several rock modifications are highly efficient in increasing the agronomic effectiveness of SRPs. Enhanced weathering of SRPs could additionally sequester substantial amounts of CO2 from the atmosphere and silicon (Si) supply can induce a broad spectrum of plant biotic and abiotic stress resistance. Recycling massive amounts of rock residues from domestic mining industries could furthermore resolve serious disposal challenges and improve fertilizer self-sufficiency. In conclusion, under the right circumstances, SRPs could not only advance low-cost and regional soil sustaining crop production but contribute to various sustainable development goals.
Remineralizing soils? The agricultural usage of silicate rock powders in the context of One Health
(2022)
The concept of soil health describes the capacity of soil to fulfill essential functions and ecosystem services. Healthy soils are inextricably linked to sustainable agriculture and are crucial for the interconnected health of plants, animals, humans, and their environment ("One Health"). However, soil health is threatened through unprecedented rates of soil degradation. A major form of soil degradation is nutrient depletion, which has been seriously underestimated for potassium (K) and several micronutrients. One way to replenish K and micronutrients are multi-nutrient silicate rock powders (SRPs). Their agronomic suitability has long been questioned due to slow weathering rates, although recent studies found significant soil health improvements and challenge past objections which insufficiently addressed the factorial complexity of the weathering process. Furthermore, environmental co-benefits might arise through their mixture with livestock slurry, which could reduce the slurry’s ammonia (NH3) emissions and improve its biophysicochemical properties. However, neither SRPs effects on soil health, nor the biophysicochemical effects of mixing SRPs with livestock slurry have hitherto been comprehensively analyzed. The overall aim of this dissertation is thus to review the agricultural usage of SRPs in the context of One Health. The first part of this thesis starts with an elaboration of the health concept in general and then explores the interlinkages between soil health and One Health. Subsequently, the potentials and oftentimes bypassed problems of operationalizing soil health will be outlined, and feasible ways for its future usage are proposed. In the second part of the thesis, it is reviewed how and under which circumstances SRPs can ameliorate soil health. This is done by presenting a new framework with the most relevant factors for the usage of SRPs through which several contradictory outcomes of prior studies can be explained. A subsequent analysis of 48 crop trials reveals the potential of SRPs as K and multi-nutrient soil amendment for tropical soils, whereas the benefits for temperate soils are inconclusive. The review revealed various co-benefits that could substantially increase SRPs overall agronomic efficiency. The last part of the thesis reports about the effects of mixing two rock powders with cattle slurry. SRPs significantly increased the slurry´s CH4 emission rates, whereas the effects on NH3, CO2, and N2O emission rates were mostly insignificant. The rock powders increased the nutrient content of the slurry and altered its microbiology. In conclusion, the concept of soil health must be operationalized in more specific, practical, and context-dependent ways. Particularly in humid tropical environments, SRPs could advance low-cost soil health ameliorations, and its usage could have additional co-benefits regarding One Health. Mixing SRPs with organic materials like livestock slurry could overcome the major obstacle of their low solubility, although the effects on NH3 and greenhouse gas emissions must be further evaluated.
Recovery Across Different Temporal Settings: How Lunchtime Activities Influence Evening Activities
(2022)
Recovery from work stress during workday breaks, free evenings, weekends, and vacations is known to benefit employee health and well-being. However, how recovery at different temporal settings is interconnected is not well understood. We hypothesized that on days when employees engage in recovery-enhancing lunchtime activities, they will experience higher resources when leaving home from work (i.e., low fatigue and high positive affect) and consequently spend more time on recovery-enhancing activities in the evening, thus creating a positive recovery cycle. In this study, 97 employees were randomized into lunchtime park walk and relaxation groups. As evening activities, we measured time spent on physical exercise, physical activity in natural surroundings, and social activities. Afternoon resources and time spent on evening activities were assessed twice a week before, during, and after the intervention, for five weeks. Our results based on multilevel analyses showed that on days when employees completed the lunchtime park walk, they spent more time on evening physical exercise and physical activity in natural surroundings compared to days when the lunch break was spent as usual. However, neither lunchtime relaxation exercises nor afternoon resources were associated with any of the evening activities. Our findings suggest that other factors than afternoon resources are more important in determining how much time employees spend on various evening activities. Fifteen-minute lunchtime park walks inspired employees to engage in similar healthbenefitting activities during their free time.
Qualität der Qualitätsprüfung: Testberichte im klassischen und modernen Videospieljournalismus
(2022)
Die Hochzeit des gedruckten Videospieljournalismus um die Jahrtausendwende ist vorüber. Seit über 15 Jahren sind die verkauften Auflagen der klassischen Videospielzeitschriften wie Gamestar oder PC Games rückläufig. Andere Magazine wurden zwischenzeitlich aus wirtschaftlichen Gründen eingestellt, darunter PC Action oder auch der einstige Marktführer Computer Bild Spiele. Trotzdem entwickelte sich eine journalistische Gegenbewegung, die Kieron Gillen im Jahr 2004 in seinem Manifest "The New Games Journalism" begründete. Es entstanden in Deutschland Videospielzeitschriften wie GAIN oder WASD, deren Berichterstattung Videospiele weniger als Produkt, sondern zunehmend als künstlerisches Objekt wahrnehmen und sie in einen gesellschaftlichen und kulturellen Kontext einordnen.
Ungeachtet dessen erfolgt in den Redaktionen eine technische und inhaltliche Sichtung der Videospiele, die dem Publikum als Testbericht präsentiert wird. Da es sich dabei aus historischer Perspektive um den Kerninhalt von Videospielzeitschriften handelt, soll dieser als Analysegegenstand dieser Arbeit dienen und ein Indiz für die Qualität der Magazine als Ganzes sein. Mit Blick auf die unterschiedlichen Entwicklungen im Videospieljournalismus soll folgende Frage beantwortet werden: Verfügen moderne Videospielzeitschriften über eine höhere Qualität als klassische Magazine? Dazu erfolgt eine qualitative Inhaltsanalyse der Testberichte und ein Vergleich mit etablierten Qualitätsmerkmalen aus dem allgemeinen Journalismus, ebenso wie dem Fach-, Nutzwert- und Videospieljournalismus.
ProtSTonKGs: A Sophisticated Transformer Trained on Protein Sequences, Text, and Knowledge Graphs
(2022)
While most approaches individually exploit unstructured data from the biomedical literature or structured data from biomedical knowledge graphs, their union can better exploit the advantages of such approaches, ultimately improving representations of biology. Using multimodal transformers for such purposes can improve performance on context dependent classication tasks, as demonstrated by our previous model, the Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs (STonKGs). In this work, we introduce ProtSTonKGs, a transformer aimed at learning all-encompassing representations of protein-protein interactions. ProtSTonKGs presents an extension to our previous work by adding textual protein descriptions and amino acid sequences (i.e., structural information) to the text- and knowledge graph-based input sequence used in STonKGs. We benchmark ProtSTonKGs against STonKGs, resulting in improved F1 scores by up to 0.066 (i.e., from 0.204 to 0.270) in several tasks such as predicting protein interactions in several contexts. Our work demonstrates how multimodal transformers can be used to integrate heterogeneous sources of information, paving the foundation for future approaches that use multiple modalities for biomedical applications.