Refine
H-BRS Bibliography
- yes (252) (remove)
Departments, institutes and facilities
- Fachbereich Informatik (68)
- Fachbereich Wirtschaftswissenschaften (58)
- Fachbereich Ingenieurwissenschaften und Kommunikation (43)
- Fachbereich Sozialpolitik und Soziale Sicherung (38)
- Fachbereich Angewandte Naturwissenschaften (36)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (34)
- Graduierteninstitut (15)
- Institut für Verbraucherinformatik (IVI) (14)
- Institute of Visual Computing (IVC) (14)
- Institut für funktionale Gen-Analytik (IFGA) (7)
- Institut für Cyber Security & Privacy (ICSP) (6)
- Institut für Medienentwicklung und -analyse (IMEA) (6)
- Internationales Zentrum für Nachhaltige Entwicklung (IZNE) (5)
- Zentrum für Innovation und Entwicklung in der Lehre (ZIEL) (4)
- Institut für Soziale Innovationen (ISI) (3)
- Zentrum für Ethik und Verantwortung (ZEV) (3)
- Centrum für Entrepreneurship, Innovation und Mittelstand (CENTIM) (2)
- Institut für Sicherheitsforschung (ISF) (2)
- Sprachenzentrum (1)
- Verwaltung (1)
Document Type
- Article (78)
- Conference Object (61)
- Part of a Book (40)
- Book (monograph, edited volume) (22)
- Doctoral Thesis (15)
- Preprint (15)
- Contribution to a Periodical (6)
- Report (5)
- Research Data (4)
- Master's Thesis (2)
Year of publication
- 2020 (252) (remove)
Has Fulltext
- no (252) (remove)
Keywords
- Digitalisierung (3)
- Lehrbuch (3)
- Quality diversity (3)
- post-buckling (3)
- ARIMA (2)
- Artificial Intelligence (2)
- Autoencoder (2)
- Automatic Short Answer Grading (2)
- Bayesian optimization (2)
- Computational fluid dynamics (2)
Bei der sechsten Ausgabe des wissenschaftlichen Workshops ”Usable Security und Privacy” auf der Mensch und Computer 2020 werden wie in den vergangenen Jahren aktuelle Forschungs- und Praxisbeiträge präsentiert und anschließend mit allen Teilnehmenden diskutiert. Drei Beiträge befassen sich dieses Jahr mit dem Thema Privatsphäre, einer mit dem Thema Sicherheit. Mit dem Workshop wird ein etabliertes Forum fortgeführt und weiterentwickelt, in dem sich Expert*innen aus unterschiedlichen Domänen, z. B. dem Usability- und Security-Engineering, transdisziplinär austauschen können.
Solving differential-algebraic equations (DAEs) efficiently by means of appropriate numerical schemes for time-integration is an ongoing topic in applied mathematics. In this context, especially when considering large systems that occur with respect to many fields of practical application effective computation becomes relevant. In particular, corresponding examples are given when having to simulate network structures that consider transport of fluid and gas or electrical circuits. Due to the stiffness properties of DAEs, time-integration of such problems generally demands for implicit strategies. Among the schemes that prove to be an adequate choice are linearly implicit Rung-Kutta methods in the form of Rosenbrock-Wanner (ROW) schemes. Compared to fully implicit methods, they are easy to implement and avoid the solution of non-linear equations by including Jacobian information within their formulation. However, Jacobian calculations are a costly operation. Hence, necessity of having to compute the exact Jacobian with every successful time-step proves to be a considerable drawback. To overcome this drawback, a ROW-type method is introduced that allows for non-exact Jacobian entries when solving semi-explicit DAEs of index one. The resulting scheme thus enables to exploit several strategies for saving computational effort. Examples include using partial explicit integration of non-stiff components, utilizing more advantageous sparse Jacobian structures or making use of time-lagged Jacobian information. In fact, due to the property of allowing for non-exact Jacobian expressions, the given scheme can be interpreted as a generalized ROW-type method for DAEs. This is because it covers many different ROW-type schemes known from literature. To derive the order conditions of the ROW-type method introduced, a theory is developed that allows to identify occurring differentials and coefficients graphically by means of rooted trees. Rooted trees for describing numerical methods were originally introduced by J.C. Butcher. They significantly simplify the determination and definition of relevant characteristics because they allow for applying straightforward procedures. In fact, the theory presented combines strategies used to represent ROW-type methods with exact Jacobian for DAEs and ROW-type methods with non-exact Jacobian for ODEs. For this purpose, new types of vertices are considered in order to describe occurring non-exact elementary differentials completely. The resulting theory thus automatically comprises relevant approaches known from literature. As a consequence, it allows to recognize order conditions of familiar methods covered and to identify new conditions. With the theory developed, new sets of coefficients are derived that allow to realize the ROW-type method introduced up to orders two and three. Some of them are constructed based on methods known from literature that satisfy additional conditions for the purpose of avoiding effects of order reduction. It is shown that these methods can be improved by means of the new order conditions derived without having to increase the number of internal stages. Convergence of the resulting methods is analyzed with respect to several academic test problems. Results verify the theory determined and the order conditions found as only schemes satisfying the order conditions predicted preserve their order when using non-exact Jacobian expressions.
Autonomous driving enables new mobility concepts such as shared-autonomous services. Although significant re-search has been done on passenger-car interaction, work on passenger interaction with robo-taxis is still rare. In this paper, we tackle the question of how passengers experience robo-taxis as a service in real-life settings to inform the interaction design. We conducted a Wizard of Oz study with an electric vehicle where the driver was hidden from the passenger to simulate the service experience of a robo-taxi. 10 participants had the opportunity to use the simulated shared-autonomous service in real-life situations for one week. By the week's end, 33 rides were completed and recorded on video. Also, we flanked the study conducting interviews before and after with all participants. The findings provided insights into four design themes that could inform the service design of robo-taxis along the different stages including hailing, pick-up, travel, and drop-off.
Abschlussbericht zum BMBF-Fördervorhaben Enabling Infrastructure for HPC-Applications (EI-HPC)
(2020)
Optimization plays an essential role in industrial design, but is not limited to minimization of a simple function, such as cost or strength. These tools are also used in conceptual phases, to better understand what is possible. To support this exploration we focus on Quality Diversity (QD) algorithms, which produce sets of varied, high performing solutions. These techniques often require the evaluation of millions of solutions -- making them impractical in design cases. In this thesis we propose methods to radically improve the data-efficiency of QD with machine learning, enabling its application to design. In our first contribution, we develop a method of modeling the performance of evolved neural networks used for control and design. The structures of these networks grow and change, making them difficult to model -- but with a new method we are able to estimate their performance based on their heredity, improving data-efficiency by several times. In our second contribution we combine model-based optimization with MAP-Elites, a QD algorithm. A model of performance is created from known designs, and MAP-Elites creates a new set of designs using this approximation. A subset of these designs are the evaluated to improve the model, and the process repeats. We show that this approach improves the efficiency of MAP-Elites by orders of magnitude. Our third contribution integrates generative models into MAP-Elites to learn domain specific encodings. A variational autoencoder is trained on the solutions produced by MAP-Elites, capturing the common “recipe” for high performance. This learned encoding can then be reused by other algorithms for rapid optimization, including MAP-Elites. Throughout this thesis, though the focus of our vision is design, we examine applications in other fields, such as robotics. These advances are not exclusive to design, but serve as foundational work on the integration of QD and machine learning.
Graph drawing with spring embedders employs a V x V computation phase over the graph's vertex set to compute repulsive forces. Here, the efficacy of forces diminishes with distance: a vertex can effectively only influence other vertices in a certain radius around its position. Therefore, the algorithm lends itself to an implementation using search data structures to reduce the runtime complexity. NVIDIA RT cores implement hierarchical tree traversal in hardware. We show how to map the problem of finding graph layouts with force-directed methods to a ray tracing problem that can subsequently be implemented with dedicated ray tracing hardware. With that, we observe speedups of 4x to 13x over a CUDA software implementation.
Unter dem Begriff „Additive Fertigung“ werden alle Verfahren zusammengefasst, die dazu dienen Formteile aufgrund von CAD-Daten schichtweise aufzubauen. Dabei geschieht der Aufbau stets selektiv, entsprechend der durch die CAD-Daten vorgegebenen Positionen. Während für Metalle und Kunststoffe diese Technik bereits in der industriellen Anwendung etabliert ist, befindet sie sich im Bereich der keramischen Werkstoffe noch in einer frühen Entwicklungs- bzw. Anwendungsphase. Um so wichtiger ist es, den aktuellen Stand der Technik und das Potenzial für keramische Bauteile umfassend darzustellen.
In optimization methods that return diverse solution sets, three interpretations of diversity can be distinguished: multi-objective optimization which searches diversity in objective space, multimodal optimization which tries spreading out the solutions in genetic space, and quality diversity which performs diversity maintenance in phenotypic space. We introduce niching methods that provide more flexibility to the analysis of diversity and a simple domain to compare and provide insights about the paradigms. We show that multiobjective optimization does not always produce much diversity, quality diversity is not sensitive to genetic neutrality and creates the most diverse set of solutions, and multimodal optimization produces higher fitness solutions. An autoencoder is used to discover phenotypic features automatically, producing an even more diverse solution set. Finally, we make recommendations about when to use which approach.
Analytische Chemie II
(2020)
Dieses Arbeitsbuch führt durch das erfolgreiche Lehrbuch Skoog/Holler/Crouch, Instrumentelle Analytik und ist vor allem für das Selbststudium konzipiert. In fünf Teilen werden die Vorlesungsinhalte der fortgeschritteneren Analytischen Chemie zusammengefasst und anhand ausgewählter Beispiele erläutert: Mit der Untersuchung von Molekülen befassen sich Massenspektrometrie und Kernresonanzspektroskopie, zudem werden zahlreiche elektroanalytische Methoden wie Potentiometrie, Coulometrie, Amperometrie und Voltammetrie behandelt. In einem Überblick über speziellere Verfahren der Analytik geht es unter anderem ebenso um den Einsatz radioaktiver Substanzen und die Nutzung verschiedener Fluoreszenzverfahren wie um Methoden der Informationsgewinnung in der zunehmend wichtigen elektrochemischen und optischen Sensortechnik sowie deren Automatisierbarkeit. Den Abschluss bildet eine Zusammenfassung verschiedener Prinzipien und Anwendungsmethoden der Statistik, die im Rahmen der Analytik schlichtweg unverzichtbar sind. Um das selbstständige Lernen zu erleichtern, wird dabei in allen Teilen des Buches immer wieder auf essenzielle Abschnitte und Abbildungen des Lehrbuches verwiesen.
Due to the use of fossil fuel resources, many environmental problems have been increasingly growing. Thus, the recent research focuses on the use of environment friendly materials from sustainable feedstocks for future fuels, chemicals, fibers and polymers. Lignocellulosic biomass has become the raw material of choice for these new materials. Recently, the research has focused on using lignin as a substitute material in many industrial applications. The antiradical and antimicrobial activity of lignin and lignin-based films are both of great interest for applications such as food packaging additives. DPPH assay was used to determine the antioxidant activity of Kraft lignin compared to Organosolv lignins from different biomasses. The purification procedure of Kraft lignin showed that double-fold selective extraction is the most efficient confirmed by UV-Vis, FTIR, HSQC, 31PNMR, SEC, and XRD. The antioxidant capacity was discussed regarding the biomass source, pulping process, and degree of purification. Lignin obtained from industrial black liquor are compared with beech wood samples: Biomass source influences the DPPH inhibition (softwood > grass) and the TPC (softwood < grass). DPPH inhibition affected by the polarity of the extraction solvent. Following the trend: ethanol > diethylether > acetone. Reduced polydispersity has positive influence on the DPPH inhibition. Storage decreased the DPPH inhibition but increased the TPC values. The DPPH assay was also used to discuss the antiradical activity of HPMC/lignin and HPMC/lignin/chitosan films. In both binary (HPMC/lignin) and ternary (HPMC/lignin/chitosan) systems the 5% addition showed the highest activity and the highest addition had the lowest. Both scavenging activity and antimicrobial activity are dependent on the biomass source; Organosolv of softwood > Kraft of softwood > Organosolv of grass. Lignins and lignin-containing films showed high antimicrobial activities against Gram-positive and Gram-negative bacteria at 35 °C and at low temperatures (0-7 °C). Purification of Kraft lignin has a negative effect on the antimicrobial activity while storage has positive effect. The lignin leaching in the produced films affected the activity positively and the chitosan addition enhances the activity for both Gram-positive and Gram-negative bacteria. Testing the films against food spoilage bacteria that grow at low temperatures revealed the activity of the 30% addition on HPMC/L1 film against both B. thermosphacta and P. fluorescens while L5 was active only against B. thermosphacta. In HPMC/lignin/chitosan films, the 5% addition exhibited activity against both food spoilage bacteria.
With the digital transformation, software systems have become an integral part of our society and economy. In every part of our life, software systems are increasingly utilized to, e.g., simplify housework or to optimize business processes. All these applications are connected to the Internet, which already includes millions of software services consumed by billions of people. Applications which process such a magnitude of users and data traffic requires to be highly scalable and are therefore denoted as Ultra Large Scale (ULS) systems. Roy Fielding has defined one of the first approaches which allows designing modern ULS software systems. In his doctoral thesis, Fielding introduced the architectural style Representational State Transfer (REST) which builds the theoretical foundation of the web. At present, the web is considered as the world's largest ULS system. Due to a large number of users and the significance of software for society and the economy, the security of ULS systems is another crucial quality factor besides high scalability.
The way solutions are represented, or encoded, is usually the result of domain knowledge and experience. In this work, we combine MAP-Elites with Variational Autoencoders to learn a Data-Driven Encoding (DDE) that captures the essence of the highest-performing solutions while still able to encode a wide array of solutions. Our approach learns this data-driven encoding during optimization by balancing between exploiting the DDE to generalize the knowledge contained in the current archive of elites and exploring new representations that are not yet captured by the DDE. Learning representation during optimization allows the algorithm to solve high-dimensional problems, and provides a low-dimensional representation which can be then be re-used. We evaluate the DDE approach by evolving solutions for inverse kinematics of a planar arm (200 joint angles) and for gaits of a 6-legged robot in action space (a sequence of 60 positions for each of the 12 joints). We show that the DDE approach not only accelerates and improves optimization, but produces a powerful encoding that captures a bias for high performance while expressing a variety of solutions.
Neben der individuellen Bedeutung von Gesundheit für jeden Menschen, steigt auch die Relevanz von „gesunden Beschäftigten“. Gerade in Zeiten von Vollbeschäftigung, Fachkräftemangel und höherem Renteneintrittsalter, rückt die Gesundheit der Beschäftigten und die damit verbundene Arbeitsfähigkeit jedes Einzelnen stärker in den Fokus. Staat, Sozialversicherungsträger und Unternehmen sind zunehmend daran interessiert, Arbeitsplätze und Arbeitsbedingungen gesundheitsförderlich zu gestalten. Hierbei bildet die BGF den Rahmen für die existierenden gesundheitsförderlichen Interventionen, die in einer Vielzahl im betrieblichen Setting vorzufinden sind. Die Arbeitspause kann in diesem Kontext als geeignete Intervention angesehen werden, die jedoch sehr vielfältig in der Ausgestaltung sein kann.
Kommunikation gilt nicht ohne Grund als die Königsdisziplin im BGM. Hier gilt es, Mitarbeiter in einem ersten Schritt für das Thema Gesundheit zu sensibilisieren und mit relevanten Materialien zu informieren, um sie letztendlich zur Teilnahme an Gesundheitsangeboten zu motivieren. Diese drei Schritte empfehlen sich ebenfalls für die Kommunikation in digitalen Zeiten. Gesundheitsplattformen und/oder Gesundheits-Apps können die Kommunikation unterstützen. Das richtige Maß an Kommunikation stellt eine weitere Herausforderung in digitalen Zeiten dar, da Informationen in der Flut an E-Mails durchaus untergehen können. Eine Kombination aus Push- und Pull-Kommunikation hat sich hierbei bewährt, um bei Mitarbeitern das nötige Interesse für Gesundheit anzustoßen, damit diese dann eigenständig aus bestehenden Angeboten (Informationen, Kurse usw.) wählen.
Bionik
(2020)
Wie machen die das... kann angesichts der erstaunlichen Fähigkeiten mancher Lebewesen gefragt werden. Die Bionik fragt noch weiter …und wie kann man das nachmachen? Hier liegt ein Schwerpunkt dieses Lehrbuches, das die Bionik nicht nur an zahlreichen Beispielen erklärt, sondern auch eine Vorgehensweise für die Identifizierung biologischer Lösungen und deren Übertragung auf technische Anwendungen vermittelt. Basisinformationen der Biologie und Grundlagen der Konstruktionstechnik gewährleisten einen leichten Zugang zum Stoff. Mit dem 3D-Druck als Schlüsseltechnologie und der Thematisierung der Nachhaltigkeit geht das Buch zudem auf aktuelle Entwicklungen ein. Dieser ganzheitliche Blick auf die Bionik soll den Leser zur Durchführung bionischer Projekte befähigen und motivieren. (Verlagsangaben)
Object detectors have improved considerably in the last years by using advanced CNN architectures. However, many detector hyper-parameters are generally manually tuned, or they are used with values set by the detector authors. Automatic Hyper-parameter optimization has not been explored in improving CNN-based object detectors hyper-parameters. In this work, we propose the use of Black-box optimization methods to tune the prior/default box scales in Faster R-CNN and SSD, using Bayesian Optimization, SMAC, and CMA-ES. We show that by tuning the input image size and prior box anchor scale on Faster R-CNN mAP increases by 2% on PASCAL VOC 2007, and by 3% with SSD. On the COCO dataset with SSD there are mAP improvement in the medium and large objects, but mAP decreases by 1% in small objects. We also perform a regression analysis to find the significant hyper-parameters to tune.
Business Management
(2020)
Reinforcement learning (RL) algorithms should learn as much as possible about the environment but not the properties of the physics engines that generate the environment. There are multiple algorithms that solve the task in a physics engine based environment but there is no work done so far to understand if the RL algorithms can generalize across physics engines. In this work, we compare the generalization performance of various deep reinforcement learning algorithms on a variety of control tasks. Our results show that MuJoCo is the best engine to transfer the learning to other engines. On the other hand, none of the algorithms generalize when trained on PyBullet. We also found out that various algorithms have a promising generalizability if the effect of random seeds can be minimized on their performance.
Carbachol dimers with primary carbamate groups as homobivalent modulators of muscarinic receptors
(2020)
Carbon capture and storage
(2020)
This Special Report explores the most recent regulatory, political and economic trends and themes arising from CCS technologies and projects to help the reader succeed in this rapidly changing market.
Chancengerechte Online-Lehre
(2020)
Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer Grading
(2020)
Automatic Short Answer Grading (ASAG) is the process of grading the student answers by computational approaches given a question and the desired answer. Previous works implemented the methods of concept mapping, facet mapping, and some used the conventional word embeddings for extracting semantic features. They extracted multiple features manually to train on the corresponding datasets. We use pretrained embeddings of the transfer learning models, ELMo, BERT, GPT, and GPT-2 to assess their efficiency on this task. We train with a single feature, cosine similarity, extracted from the embeddings of these models. We compare the RMSE scores and correlation measurements of the four models with previous works on Mohler dataset. Our work demonstrates that ELMo outperformed the other three models. We also, briefly describe the four transfer learning models and conclude with the possible causes of poor results of transfer learning models.
Comparing Non-Visual and Visual Guidance Methods for Narrow Field of View Augmented Reality Displays
(2020)
Unternehmensberatungen stellen mit ihrem Leistungsportfolio einen bedeutenden Wirtschaftsfaktor dar. Die digitale Transformation und die sehr spezifischen Marktstrukturen der Gesundheitswirtschaft verlangen nach differenzierten Beratungsansätzen, die zugleich ein großes Beratungsfeld eröffnen und so das Wachstum und die Attraktivität von Unternehmensberatungen in diesem Segment stärken. Das Buch zeigt Themenfelder und Erfolgsfaktoren bei Beratungsprojekten auf. Dabei wird der Ansatz der Komplementärberatung in den Mittelpunkt gestellt, um den vielfältigen Change-Anforderungen bei der digitalen Transformation im Gesundheitswesen ganzheitlich gerecht zu werden. (Verlagsangaben)
An essential measure of autonomy in assistive service robots is adaptivity to the various contexts of human-oriented tasks, which are subject to subtle variations in task parameters that determine optimal behaviour. In this work, we propose an apprenticeship learning approach to achieving context-aware action generalization on the task of robot-to-human object hand-over. The procedure combines learning from demonstration and reinforcement learning: a robot first imitates a demonstrator’s execution of the task and then learns contextualized variants of the demonstrated action through experience. We use dynamic movement primitives as compact motion representations, and a model-based C-REPS algorithm for learning policies that can specify hand-over position, conditioned on context variables. Policies are learned using simulated task executions, before transferring them to the robot and evaluating emergent behaviours. We additionally conduct a user study involving participants assuming different postures and receiving an object from a robot, which executes hand-overs by either imitating a demonstrated motion, or adapting its motion to hand-over positions suggested by the learned policy. The results confirm the hypothesized improvements in the robot’s perceived behaviour when it is context-aware and adaptive, and provide useful insights that can inform future developments.
Coumarin as a structural component of substrates and probes for serine and cysteine proteases
(2020)
This article explores the opportunities, challenges, as well as the activities of the Chinese governmental and commercial stakeholders to promote cross-border e-commerce trade between China and Africa, based on the classification and correlation analysis of the literature from 2011 to 2019. The results show that the biggest driver for the development of China-Africa cross-border e-commerce trade is the gap between the rapid growth of the African population, especially the middle class, and the limited local capability to satisfy their demand. The rapid development of the internet and mobile internet is another driving factor. The biggest challenge is the last mile delivery of logistics, and online payment issues in Africa. At the macro-level the Chinese government has promoted measures such as infrastructure investment, e-commerce test zones and the establishment of pilot projects. At the firm level, Chinese companies have focused on solving practical micro-level local operational problems such as logistics, online payment, and talent training. The results also show that the referred literature is still in its infancy, mostly theoretical and less practical, and requires more in-depth domain specific analysis in the future.