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Wow, You Are Terrible at This!: An Intercultural Study on Virtual Agents Giving Mixed Feedback
(2020)
While the effects of virtual agents in terms of likeability, uncanniness, etc. are well explored, it is unclear how their appearance and the feedback they give affects people's reactions. Is critical feedback from an agent embodied as a mouse or a robot taken less serious than from a human agent? In an intercultural study with 120 participants from Germany and the US, participants had to find hidden objects in a game and received feedback on their performance by virtual agents with different appearances. As some levels were designed to be unsolvable, critical feedback was unavoidable. We hypothesized that feedback would be taken more serious, the more human the agent looked. Also, we expected the subjects from the US to react more sensitively to criticism. Surprisingly, our results showed that the agents' appearance did not significantly change the participants' perception. Also, while we found highly significant differences in inspirational and motivational effects as well as in perceived task load between the two cultures, the reactions to criticism were contrary to expectations based on established cultural models. This work improves our understanding on how affective virtual agents are to be designed, both with respect to culture and to dialogue strategies.
This paper presents a new approach for the teaching of competence in additive manufacturing to engineering students in product development. Particularly new to this approach is the combination of the students' autonomous assembly and commissioning of a 3D-printer, and the independent development of guidelines for this new technology regarding the design of components. This way the students will be able to gain first practical experiences with the data preparation, the additive manufacturing process itself and also the required post-treatment of the 3D-printed parts. To allow the students a significantly deeper insight into the functioning of 3D-printing, the workshop Rapid Prototyping developed a new approach in the course of which the students first assemble a construction kit for a 3D-printer themselves and then commission the printer. This enables the students to gain a better understanding of the functionality and configuration of additive manufacturing. In a next step, the students used the 3D-printers they constructed themselves to produce components which they take from a database. Finally, the experiences of the students in the course of the workshop will be evaluated to review the effectiveness of the new approach.
Additive manufacturing (AM) or 3D printing (3DP) has become a widespread new technology in recent years and is now used in many areas of industry. At the same time, there is an increasing need for training courses that impart the knowledge required for product development in 3D printing. In this article, a workshop on “Rapid Prototyping” is presented, which is intended to provide students with the technical and creative knowledge for product development in the field of AM. Today, additive manufacturing is an important part of teaching for the training of future engineers. In a detailed literature review, the advantages and disadvantages of previous approaches to training students are examined and analyzed. On this basis, a new approach is developed in which the students analyze and optimize a given product in terms of additivie manufacturing. The students use two different 3D printers to complete this task. In this way, the students acquire the skills to work independently with different processes and materials. With this new approach, the students learn to adapt the design to different manufacturing processes and to observe the restrictions of different materials. The results of these courses are evaluated through feedback in a presentation and a questionnaire.
Home Care Applications and Ambient Assisted Living become increasingly attractive. This is caused as well by market pull, as the number of elderly people grows monotonously, as well as by technology push, as technological advances and attractive products pave the way to economically advantageous offerings. However, in real-life applications, a significant number of challenges remain. Those include seamless communication between products from different supplier, due to the lack of sufficiently standardized solutions, energy budgets, and scalability of solutions. This paper presents the experience from the InCASA project (Integrated Network for Completely Assisted Senior Citizen's Autonomy), where architectures for heterogeneous physical and logical communication flows are examined.
A recognizable division appears between students with a comprehensive knowledge of the Web and those that are less certain about its resources. This is where, the teaching innovation Web Mentoring: Peer-to-Peer has been developed to help the students to cope better with the demands of media education. Furthermore, this presents the opportunity for master’s degree students to begin mentoring undergraduate students. Mentoring sessions have already been carried out successfully in the previous two semesters and are being presented, evaluated and discussed.
This paper describes a comparative study of two tactile systems supporting navigation for persons with little or no visual and auditory perception. The efficacy of a tactile head-mounted device (HMD) was compared to that of a wearable device, a tactile belt. A study with twenty participants showed that the participants took significantly less time to complete a course when navigating with the HMD, as compared to the belt.
Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.
A novel approach of a test environment for embedded networking nodes has been conceptualized and implemented. Its basis is the use of virtual nodes in a PC environment, where each node executes the original embedded code. Different nodes run in parallel, connected via so-called virtual channels. The environment allows to modifying the behavior of the virtual channels as well as the overall topology during runtime to virtualize real-life networking scenarios. The presented approach is very efficient and allows a simple description of test cases without the need of a network simulator. Furthermore, it speeds up the process of developing new features as well as it supports the identification of bugs in wireless communication stacks. In combination with powerful test execution systems, it is possible to create a continuous development and integration flow.