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Intelligent autonomous software robots replacing human activities and performing administrative processes are reality in today’s corporate world. This includes, for example, decisions about invoice payments, identification of customers for a marketing campaign, and answering customer complaints. What happens if such a software robot causes a damage? Due to the complete absence of human activities, the question is not trivial. It could even happen that no one is liable for a damage towards a third party, which could create an uncalculatable legal risk for business partners. Furthermore, the implementation and operation of those software robots involves various stakeholders, which result in the unsolvable endeavor of identifying the originator of a damage. Overall it is advisable to all involved parties to carefully consider the legal situation. This chapter discusses the liability of software robots from an interdisciplinary perspective. Based on different technical scenarios the legal aspects of liability are discussed.
Hydraulic modeling is the classical approach to investigate and describe complex fluid motion. Many empirical formulas in the literature used for the hydraulic design of river training measures and structures have been developed using experimental data from the laboratory. Although computer capacities have increased to a high level which allows to run complex numerical simulations on standard workstation nowadays, non-standard design of structures may still raise the need to perform physical model investigations. These investigations deliver insight into details of flow patterns and the effect of varying boundary conditions. Data from hydraulic model tests may be used for calibration of numerical models as well. As the field of hydraulic modeling is very complex, this chapter intends to give a short overview on capacities and limits of hydraulic modeling in regard to river flows and hydraulic structures only. The reader shall get a first idea of modeling principles and basic considerations. More detailed information can be found in the references.
The existence of several mobile operating systems, such as Android and iOS, is a challenge for developers because the individual platforms are not compatible with each other and require separate app developments. For this reason, cross-platform approaches have become popular but lack in cloning the native behavior of the different operating systems. Out of the plenty cross-platform approaches, the progressive web app (PWA) approach is perceived as promising but needs further investigation. Therefore, the paper at hand aims at investigating whether PWAs are a suitable alternative for native apps by developing a PWA clone of an existing app. Two surveys are conducted in which potential users test and evaluate the PWA prototype with regard to its usability. The survey results indicate that PWAs have great potential, but cannot be treated as a general alternative to native apps. For guiding developers when and how to use PWAs, four design guidelines for the development of PWA-based apps are derived based on the results.
RGB-D sensors such as the Microsoft Kinect or the Asus Xtion are inexpensive 3D sensors. A depth image is computed by calculating the distortion of a known infrared light (IR) pattern which is projected into the scene. While these sensors are great devices they have some limitations. The distance they can measure is limited and they suffer from reflection problems on transparent, shiny, or very matte and absorbing objects. If more than one RGB-D camera is used the IR patterns interfere with each other. This results in a massive loss of depth information. In this paper, we present a simple and powerful method to overcome these problems. We propose a stereo RGB-D camera system which uses the pros of RGB-D cameras and combine them with the pros of stereo camera systems. The idea is to utilize the IR images of each two sensors as a stereo pair to generate a depth map. The IR patterns emitted by IR projectors are exploited here to enhance the dense stereo matching even if the observed objects or surfaces are texture-less or transparent. The resulting disparity map is then fused with the depth map offered by the RGB-D sensor to fill the regions and the holes that appear because of interference, or due to transparent or reflective objects. Our results show that the density of depth information is increased especially for transparent, shiny or matte objects.
Additive manufacturing (AM) works by creating objects layer by layer in a manner similar to a 2D printer with the “printed” layers stacked on top of each other. The layer-wise manufacturing nature of AM enables fabrication of freeform geometries which cannot be fabricated using conventional manufacturing methods as a one part. Depending on how each layer is created and bonded to the adjacent layers, different AM methods have been developed. In this chapter, the basic terms, common materials, and different methods of AM are described, and their potential applications are discussed.