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Technology and computer applications influence our daily lives and questions arise concerning the role of artificial intelligence and decision-making algorithms. There are warning voices, that computers can, in theory, emulate human intelligence-and exceed it. This paper points out that a replacement of humans by computers is unlikely, because human thinking is characterized by cognitive heuristics and emotions, which cannot simply be implemented in machines operating with algorithms, procedural data processing or artificial neural networks. However, we are going to share our responsibilities with superior computer systems, which are tracking and surveying all of our digital activities, whereas we have no idea of the decision-making processes inside the machines. It is shown that we need a new digital humanism defining rules of computer responsibilities to avoid digital totalism and comprehensive monitoring and controlling of individuals within the planet Earth.
Our university carries out various research projects. Among others, the project Schluckspecht is an interdisciplinary work on different ultra-efficient car concepts for international contests. Besides the engineering work, one part of the project deals with real-time data visualization. In order to increase the efficiency of the vehicle, an online monitoring of the runtime parameters is necessary. The driving parameters of the vehicle are transmitted to a processing station via a wireless network connection. We plan to use an augmented reality (AR) application to visualize different data on top of the view of the real car. By utilizing a mobile Android or iOS device a user can interactively view various real-time and statistical data. The car and its components are meant to be augmented by various additional information, whereby that information should appear at the correct position of the components. An engine e.g. could show the current rpm and consumption values. A battery on the other hand could show the current charge level. The goal of this paper is to evaluate different possible approaches, their suitability and to expand our application to other projects at our university.
In the course of the last few years, our students are becoming increasingly unhappy. Sometimes they stop attending lectures and even seem not to know how to behave correctly. It feels like they are getting on strike. Consequently, drop-out rates are sky-rocketing. The lecturers/professors are not happy either, adopting an “I-don’t-care” attitude.
An interdisciplinary, international team set in to find out: (1) What are the students unhappy about? Why is it becoming so difficult for them to cope? (2) What does the “I-don’t-care” attitude of professors actually mean? What do they care or not care about? (3) How far do the views of the parties correlate? Could some kind of mutual understanding be achieved?
The findings indicate that, at least at our universities, there is rather a long way to go from “Engineering versus Pedagogy” to “Engineering Pedagogy”.
Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset where, a subspace is the subset of dimensions of the data. But exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, thus, parallelization becomes difficult and inefficient. SUBSCALE is a recent subspace clustering algorithm which is scalable with the dimensions and contains independent processing steps which can be exploited through parallelism. In this paper, we aim to leverage, firstly, the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation has shown linear speedup. Secondly, we are developing an approach using graphics processing units (GPUs) for fine-grained data parallelism to accelerate the computation further. First tests of the GPU implementation show very promising results.
The need to measure basic aerosol parameters has increased dramatically in the last decade. This is due mainly to their harmful effect on the environment and on public health. Legislation requires that particle emissions and ambient levels, workplace particle concentrations and exposure to them are measured to confirm that the defined limits are met and the public is not exposed to harmful concentrations of aerosols.
In this paper we show that a model-free approach to learn behaviors in joint space can be successfully used to utilize toes of a humanoid robot. Keeping the approach model-free makes it applicable to any kind of humanoid robot, or robot in general. Here we focus on the benefit on robots with toes which is otherwise more difficult to exploit. The task has been to learn different kick behaviors on simulated Nao robots with toes in the RoboCup 3D soccer simulator. As a result, the robot learned to step on its toe for a kick that performs 30% better than learning the same kick without toes.