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“Building Bridges Across Continents” (BBAC) is an intercultural and student-centered project that seeks to promote international communication and helps students develop competencies in entrepreneurship, international trade and global cultural awareness. The project, which is in its fourth phase of implementation, connects students from the United States, Germany, Ghana and Kenya with the help of Information Communication Technologies (ICT) in order to work on a common research assignment for a period of ten calendar weeks. The main ICTs used in the project are Skype, Facebook, wiki, email and WhatsApp. This paper describes and analyzes the background, structure, and results of the project.
One of the primary current astrobiological goals is to understand the limits of microbial resistance to extraterrestrial conditions. Much attention is paid to ionizing radiation, since it can prevent the preservation and spread of life outside the Earth. The aim of this research was to study the impact of accelerated He ions (150 MeV/n, up to 1 kGy) as a component of the galactic cosmic rays on the black fungus C. antarcticus when mixed with Antarctic sandstones—the substratum of its natural habitat—and two Martian regolith simulants, which mimics two different evolutionary stages of Mars. The high dose of 1 kGy was used to assess the effect of dose accumulation in dormant cells within minerals, under long-term irradiation estimated on a geological time scale. The data obtained suggests that viable Earth-like microorganisms can be preserved in the dormant state in the near-surface scenario for approximately 322,000 and 110,000 Earth years within Martian regolith that mimic early and present Mars environmental conditions, respectively. In addition, the results of the study indicate the possibility of maintaining traces within regolith, as demonstrated by the identification of melanin pigments through UltraViolet-visible (UV-vis) spectrophotometric approach.
The modern concept of the evolution of Mars assumes that life could potentially have originated on the planet Mars, possibly during the end of the late heavy bombardment, and could then be transferred to other planets. Since then, physical and chemical conditions on Mars changed and now strongly limit the presence of terrestrial-like life forms. These adverse conditions include scarcity of liquid water (although brine solutions may exist), low temperature and atmospheric pressure, and cosmic radiation. Ionizing radiation is very important among these life-constraining factors because it damages DNA and other cellular components, particularly in liquid conditions where radiation-induced reactive oxidants diffuse freely. Here, we investigated the impact of high doses (up to 2 kGy) of densely-ionizing (197.6 keV/µm), space-relevant iron ions (corresponding on the irradiation that reach the uppermost layer of the Mars subsurface) on the survival of an extremophilic terrestrial organism-Cryomyces antarcticus-in liquid medium and under atmospheric conditions, through different techniques. Results showed that it survived in a metabolically active state when subjected to high doses of Fe ions and was able to repair eventual DNA damages. It implies that some terrestrial life forms can withstand prolonged exposure to space-relevant ion radiation.
Compliant manipulation is a crucial skill for robots when they are supposed to act as helping hands in everyday household tasks. Still, nowadays, those skills are hand-crafted by experts which frequently requires labor-intensive, manual parameter tuning. Moreover, some tasks are too complex to be specified fully using a task specification. Learning these skills, by contrast, requires a high number of costly and potentially unsafe interactions with the environment. We present a compliant manipulation approach using reinforcement learning guided by the Task Frame Formalism, a task specification method. This allows us to specify the easy to model knowledge about a task while the robot learns the unmodeled components by reinforcement learning. We evaluate the approach by performing a compliant manipulation task with a KUKA LWR 4+ manipulator. The robot was able to learn force control policies directly on the robot without using any simulation.
As cameras are ubiquitous in autonomous systems, object detection is a crucial task. Object detectors are widely used in applications such as autonomous driving, healthcare, and robotics. Given an image, an object detector outputs both the bounding box coordinates as well as classification probabilities for each object detected. The state-of-the-art detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented advancements in detector performance, the inability to explain how their outputs are generated limits their use in safety-critical applications in particular. It is therefore crucial to explain the reason behind each detector decision in order to gain user trust, enhance detector performance, and analyze their failure.
Previous work fails to explain as well as evaluate both bounding box and classification decisions individually for various detectors. Moreover, no tools explain each detector decision, evaluate the explanations, and also identify the reasons for detector failures. This restricts the flexibility to analyze detectors. The main contribution presented here is an open-source Detector Explanation Toolkit (DExT). It is used to explain the detector decisions, evaluate the explanations, and analyze detector errors. The detector decisions are explained visually by highlighting the image pixels that most influence a particular decision. The toolkit implements the proposed approach to generate a holistic explanation for all detector decisions using certain gradient-based explanation methods. To the author’s knowledge, this is the first work to conduct extensive qualitative and novel quantitative evaluations of different explanation methods across various detectors. The qualitative evaluation incorporates a visual analysis of the explanations carried out by the author as well as a human-centric evaluation. The human-centric evaluation includes a user study to understand user trust in the explanations generated across various explanation methods for different detectors. Four multi-object visualization methods are provided to merge the explanations of multiple objects detected in an image as well as the corresponding detector outputs in a single image. Finally, DExT implements the procedure to analyze detector failures using the formulated approach.
The visual analysis illustrates that the ability to explain a model is more dependent on the model itself than the actual ability of the explanation method. In addition, the explanations are affected by the object explained, the decision explained, detector architecture, training data labels, and model parameters. The results of the quantitative evaluation show that the Single Shot MultiBox Detector (SSD) is more faithfully explained compared to other detectors regardless of the explanation methods. In addition, a single explanation method cannot generate more faithful explanations than other methods for both the bounding box and the classification decision across different detectors. Both the quantitative and human-centric evaluations identify that SmoothGrad with Guided Backpropagation (GBP) provides more trustworthy explanations among selected methods across all detectors. Finally, a convex polygon-based multi-object visualization method provides more human-understandable visualization than other methods.
The author expects that DExT will motivate practitioners to evaluate object detectors from the interpretability perspective by explaining both bounding box and classification decisions.
Saliency methods are frequently used to explain Deep Neural Network-based models. Adebayo et al.'s work on evaluating saliency methods for classification models illustrate certain explanation methods fail the model and data randomization tests. However, on extending the tests for various state of the art object detectors we illustrate that the ability to explain a model is more dependent on the model itself than the explanation method. We perform sanity checks for object detection and define new qualitative criteria to evaluate the saliency explanations, both for object classification and bounding box decisions, using Guided Backpropagation, Integrated Gradients, and their Smoothgrad versions, together with Faster R-CNN, SSD, and EfficientDet-D0, trained on COCO. In addition, the sensitivity of the explanation method to model parameters and data labels varies class-wise motivating to perform the sanity checks for each class. We find that EfficientDet-D0 is the most interpretable method independent of the saliency method, which passes the sanity checks with little problems.
State-of-the-art object detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented advancements in detector performance, the inability to explain how their outputs are generated limits their use in safety-critical applications. Previous work fails to produce explanations for both bounding box and classification decisions, and generally make individual explanations for various detectors. In this paper, we propose an open-source Detector Explanation Toolkit (DExT) which implements the proposed approach to generate a holistic explanation for all detector decisions using certain gradient-based explanation methods. We suggests various multi-object visualization methods to merge the explanations of multiple objects detected in an image as well as the corresponding detections in a single image. The quantitative evaluation show that the Single Shot MultiBox Detector (SSD) is more faithfully explained compared to other detectors regardless of the explanation methods. Both quantitative and human-centric evaluations identify that SmoothGrad with Guided Backpropagation (GBP) provides more trustworthy explanations among selected methods across all detectors. We expect that DExT will motivate practitioners to evaluate object detectors from the interpretability perspective by explaining both bounding box and classification decisions.
21 pages, with supplementary
Application systems are often advertised with features, and features are used heavily for requirements man- agement. However, often software manufacturers only have incomplete information about the features of their software. The information is distributed over different sources, such as requirements documents, issue trackers, user manuals, and code. In this paper, we research the occurrence of feature information in open source software engineering data. We report on a case study with three open source systems. We analyze what information about features can be found in issue trackers and user documentation. Furthermore, we study the abstraction levels on which the features are described, how feature information is related, and we discuss the possibility to discover such information semi-automatically. To mirror the diversity of software development contexts, we choose open source systems, which are quite different, e.g., in the rigor of issue tracker usage. The results differ accordingly. One main result is that the user documentation did not provide more accurate information than the issue tracker compared to a provided feature list. The results also give hints on how the management of feature relevant information can be supported.
Since its advent, the sustainability effects of the modern sharing economy have been the subject of controversial debate. While its potential was initially discussed in terms of post-ownership development with a view to decentralizing value creation and increasing social capital and environmental relief through better utilization of material goods, critics have become increasingly loud in recent years. Many people hoped that carsharing could lead to development away from ownership towards flexible use and thus more resource-efficient mobility. However, carsharing remains niche, and while many people like the idea in general, they appear to consider carsharing to not be advantageous as a means of transport in terms of cost, flexibility, and comfort. A key innovation that could elevate carsharing from its niche existence in the future is autonomous driving. This technology could help shared mobility gain a new boost by allowing it to overcome the weaknesses of the present carsharing business model. Flexibility and comfort could be greatly enhanced with shared autonomous vehicles (SAVs), which could simultaneously offer benefits in terms of low cost, and better use of time without the burden of vehicle ownership. However, it is not the technology itself that is sustainable; rather, sustainability depends on the way in which this technology is used. Hence, it is necessary to make a prospective assessment of the direct and indirect (un)sustainable effects before or during the development of a technology in order to incorporate these findings into the design and decision-making process. Transport research has been intensively analyzing the possible economic, social, and ecological consequences of autonomous driving for several years. However, research lacks knowledge about the consequences to be expected from shared autonomous vehicles. Moreover, previous findings are mostly based on the knowledge of experts, while potential users are rarely included in the research. To address this gap, this thesis contributes to answering the questions of what the ecological and social impacts of the expected concept of SAVs will be. In my thesis, I study in particular the ecological consequences of SAVs in terms of the potential modal shifts they can induce as well as their social consequences in terms of potential job losses in the taxi industry. Regarding this, I apply a user-oriented, mixed-method technology assessment approach that complements existing, expert-oriented technology assessment studies on autonomous driving that have so far been dominated by scenario analyses and simulations. To answer the two questions, I triangulated the method of scenario analysis and qualitative and quantitative user studies. The empirical studies provide evidence that the automation of mobility services such as carsharing may to a small extent foster a shift from the private vehicle towards mobility on demand. However, findings also indicate that rebound effects are to be expected: Significantly more users are expected to move away from the more sustainable public transportation, leading to an overcompensation of the positive modal shift effects by the negative modal shift effects. The results show that a large proportion of the taxi trips carried out can be re-placed by SAVs, making the profession of taxi driver somewhat obsolete. However, interviews with taxi drivers themselves revealed that the services provided by the drivers go beyond mere transport, so that even in the age of SAVs, the need for human assistance will continue – though to a smaller extent. Given these findings, I see action potential at different levels: users, mobility service providers, and policymakers. Regarding environmental and social impacts resulting from the use of SAVs, there is a strong conflict of objectives among users, potential SAV operators, and sustainable environmental and social policies. In order to strengthen the positive effects and counteract the negative effects, such as unintended modal shifts, policies may soon have to regulate the design of SAVs and their introduction. A key starting point for transport policy is to promote the use of more environmentally friendly means of transport, in particular by making public transportation attractive and, if necessary, by making the use of individual motorized mobility less attractive. The taxi industry must face the challenges of automation by opening up to these developments and focusing on service orientation – to strengthen the drivers’ main unique selling point compared to automated technology. Assessing the impacts of the not-yet-existing generally involves great uncertainty. With the results of my work, however, I would like to argue that a user-oriented technology assessment can usefully complement the findings of classic methods of technology assessment and can iteratively inform the development process regarding technology and regulation.
Sharing economies enabled by technical platforms have been studied regarding their economic, legal, and social effects, as well as with regard to their possible influences on CSCW topics such as work, collaboration, and trust. While a lot current research is focusing on the sharing economy and related communities, there is little work addressing the phenomenon from a socio-technical point of view. Our workshop is meant to address this gap. Building on research themes and discussion from last year’s ECSCW, we seek to engage deeper with topics such as novel socio-technical approaches for enabling sharing communities, discussing issues around digital consumer and worker protection, as well as emerging challenges and opportunities of existing platforms and approaches.
Advocates of autonomous driving predict that the occupation of taxi driver could be made obsolete by shared autonomous vehicles (SAV) in the long term. Conducting interviews with German taxi drivers, we investigate how they perceive the changes caused by advancing automation for the future of their business. Our study contributes insights into how the work of taxi drivers could change given the advent of autonomous driving: While the task of driving could be taken over by SAVs for standard trips, taxi drivers are certain that other areas of their work such as providing supplementary services and assistance to passengers would constitute a limit to such forms of automation, but probably involving a shifting role for the taxi drivers, one which focuses on the sociality of the work. Our findings illustrate how taxi drivers see the future of their work, suggesting design implications for tools that take various forms of assistance into account, and demonstrating how important it is to consider taxi drivers in the co-design of future taxis and SAV services.
The development of fully automated vehicles is becoming more and more present in the social discussion. The image of fully automated cars is determined by automobile manufacturers and placed in the context of individual traffic. In contrast to fully autonomous private cars, fully automated public transport is already operating in some cities and is to be expanded in the future. Autonomous public transport offers great potential for the development and promotion of sustainable mobility concepts. However, the user acceptance is important for the enforcement and widespread use of these technical innovations. An online study on the acceptance of fully automated public transport based on quantitative data of a sample of N = 201 is presented. The results show a high level of familiarity with the topic and a very high level of overall intention to use fully automated public transport in the future.
Mobilitäts- und Nachhaltigkeitsforscher sehen sich bei der Erforschung des Mobilitätsverhaltens von Personen mit einer bunten Palette an Erhebungsmethoden konfrontiert. Erweitert wird diese Vielfalt in der letzten Zeit durch die Möglichkeit, dieses Verhalten direkt über die Smartphones der Probanden zu erfassen. Um die Auswahl geeigneter Methoden zu erleichtern, liefert die vorliegende Literaturstudie einen detaillierten Überblick zu Fragestellungen, Daten und Erhebungsmethoden, die im Bereich der Mobilitätsforschung zur Erfassung von Alltagsmobilität eingesetzt werden.
The megatrends towards both a digital and a usership economy have changed entire markets in the past and will continue to do so over the next decades. In this work, we outline what this change means for possible futures of the mobility sector, taking the combination of trends in both economies into account. Using a sys-tematic, scenario-based trend analysis, we draft four general future scenarios and adapt the two most relevant scenarios to the automotive sector. Our findings show that combing the trends from both economies provides new insights that have often been neglected in literature because of an isolated view on digital technology only. However, service concepts such as self-driving car sharing or self-driving taxis have a great impact at various levels including microeconomic (e.g., service and product design, business models) and macroeconomic (e.g., with regard to ecological, economical, and social impacts). We give a brief outline of these issues and show which business mo dels could be successful in the most likely future scenarios, before we frame strategic implications for today’s automobile manufacturers.
Companies often have difficulties determining which criteria to base their investment decisions in different countries on. When considering direct foreign investment several risk indices are available. The PCI (Peren-Clement-Index) in its original form was developed in 1998. Its further refinement improves the PCI in three major ways: First, it offers a dynamic adjustment of criteria and consideration of recent changes in the international environment. Second, it provides business specificities of a company or its industrial sector to be considered in addition to macroeconomic aspects by a two-dimensional presentation, which ensures a customized assessment. Third, the PCI allows for consolidating investment decisions by combining a resource-orientated with a market-oriented view. The PCI allows, unlike other indices, a customized and company-specific strategic planning process. Ultimately companies must take up both perspectives in the context of an international investment decision. The use of risk indices in corporate planning for assessing global investments decision creates a fundamentally new of risk assessment.
Innovations in the mobility industry such as automated and connected cars could significantly reduce congestion and emissions by allowing the traffic to flow more freely and reducing the number of vehicles according to some researchers. However, the effectiveness of these sustainable product and service innovations is often limited by unexpected changes in consumption: some researchers thus hypothesize that the higher comfort and improved quality of time in driverless cars could lead to an increase in demand for driving with autonomous vehicles. So far, there is a lack of empirical evidence supporting either one or other of these hypotheses. To analyze the influence of autonomous driving on mobility behavior and to uncover user preferences, which serve as indicators for future travel mode choices, we conducted an online survey with a paired comparison of current and future travel modes with 302 participants in Germany. The results do not confirm the hypothesis that ownership will become an outdated model in the future. Instead they suggest that private cars, whether conventional or fully automated, will remain the preferred travel mode. At the same time, carsharing will benefit from full automation more than private cars. However, the findings indicate that the growth of carsharing will mainly be at the expense of public transport, showing that more emphasis should be placed in making public transport more attractive if sustainable mobility is to be developed.
Shared Autonomous Vehicles: Potentials for a Sustainable Mobility and Risks of Unintended Effects
(2018)
Automated and connected cars could significantly reduce congestion and emissions through a more efficient flow of traffic and a reduction in the number of vehicles. An increase in demand for driving with autonomous vehicles is also conceivable due to higher comfort and improved quality of time using driverless cars. So far, empirical evidence supporting this hypothesis is missing. To analyze the influence of autonomous driving on mobility behavior and to uncover user preferences, which serve as an indicator for future travel mode choices, we conducted an online survey with a paired comparison of current and future travel modes with 302 German participants. The results do not confirm the hypothesis that ownership will become an outdated model in the future. Instead they suggest that private cars, whether traditional or fully automated, will remain the preferred travel mode. At the same time, carsharing will benefit from full automation more than private cars. However, findings indicate that the growth of carsharing will mainly be at the expense of public transport, showing that more effort should be placed in making public transportation more attractive if sustainable mobility is to be developed.
Digitisation has brought a major upheaval to the mobility sector, and in the future, self-driving cars will probably be one of the transport modes. This study extends transport and user acceptance research by analysing in greater depth how the new modes of autonomous private cars, autonomous carsharing and autonomous taxis fit into the existing traffic mix from today's perspective. It focuses on accounting for relative added value. For this purpose, user preference theory was used as a base for an online survey (n=172) on the relative added value of the new autonomous traffic modes. Results show that users see advantages in the autonomous modes for driving comfort and time utilization whereas, in comparison to conventional cars, in many other areas – especially in terms of driving pleasure and control – they see no advantages or even relative disadvantages. Compared to public transport, the autonomous modes offer added values in almost all characteristics. This analysis at the partwor th level provides a more detailed explanation for user acceptance of automated driving.
Durch die Digitalisierung befindet sich die Mobilitätsbranche im starken Umbruch. So wird man bei der Verkehrsmittelwahl zukünftig wohl auch auf selbstfahrende Autos zurückgreifen können. Die Studie erweitert die Verkehrs- und Nutzerakzeptanzforschung, indem unter Berücksichtigung relativer Teilmehrwerte tiefergehend analysiert wird, wie sich die neuen Verkehrsmodi autonomer Privat-PKW, autonomes Carsharing und autonomes Taxi aus heutiger Sicht in den bestehenden Verkehrsmix einsortieren. Hierzu wurde auf Basis der Nutzerpräferenztheorie eine Onlineumfrage (n=172) zu den relativen Mehrwerten der neuen autonomen Verkehrsmodi durchgeführt. Es zeigt sich, dass Nutzer im Vergleich zum PKW bei den autonomen Modi Verbesserungen im Fahrkomfort und in der Zeitnutzung sehen, in vielen anderen Bereichen – insbesondere bei Fahrspaß und Kontrolle – hingegen keine Vorteile oder sogar relative Nachteile sehen. Gegenüber dem ÖPNV bieten die autonomen Modi in fast allen Eigenschaften Mehrwerte. Diese Betrachtung auf Teilnutzenebene liefert eine genauere Erklärung für Nutzerakzeptanz des automatisierten Fahrens.