Steyr Mannlicher

About: Steyr Mannlicher is a based out in . It is known for research contribution in the topics: Automation & Supply chain. The organization has 1036 authors who have published 1435 publications receiving 14227 citations.

Microblogs in Higher Education - A chance to facilitate informal and process-oriented learning?

Abstract: Microblogging is one of the latest Web 2.0 technologies. The key elements are online communication using 140 characters and the fact that it involves ''following'' anyone. There has been a great deal of excitement about this in recent months. This paper reports on a research study that was carried out on the use of a microblogging platform for process-oriented learning in Higher Education. Students of the University of Applied Sciences of Upper Austria used the tool throughout their course. All postings were carefully tracked, examined and analyzed in order to explore the possibilities offered by microblogging in education. It can be concluded that microblogging should be seen as a completely new form of communication that can support informal learning beyond classrooms.

An Introductory Review of Deep Learning for Prediction Models With Big Data

Abstract: Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem possible. On a downside, the mathematical and computational methodology underlying deep learning models is very challenging, especially for interdisciplinary scientists. For this reason, we present in this paper an introductory review of deep learning approaches including Deep Feedforward Neural Networks (D-FFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AEs), and Long Short-Term Memory (LSTM) networks. These models form the major core architectures of deep learning models currently used and should belong in any data scientist's toolbox. Importantly, those core architectural building blocks can be composed flexibly-in an almost Lego-like manner-to build new application-specific network architectures. Hence, a basic understanding of these network architectures is important to be prepared for future developments in AI.

Technostress from a Neurobiological Perspective - System Breakdown Increases the Stress Hormone Cortisol in Computer Users

Abstract: Despite the positive impact of information and communication technology (ICT) on an individual, organizational, and societal level (e.g., increased access to information, as well as enhanced performance and productivity), both scientific research and anecdotal evidence indicate that human-machine interaction, both in a private and organizational context, may lead to notable stress perceptions in users. This type of stress is referred to as technostress. A review of the literature shows that most studies used questionnaires to investigate the nature, antecedents, and consequences of technostress. Despite the value of the vast amount of questionnaire-based technostress research, we draw upon a different conceptual perspective, namely neurobiology. Specifically, we report on a laboratory experiment in which we investigated the effects of system breakdown on changes in users’ levels of cortisol, which is a major stress hormone in humans. The results of our study show that cortisol levels increase significantly as a consequence of system breakdown in a human-computer interaction task. In demonstrating this effect, our study has major implications for ICT research, development, management, and health policy. We confirm the value of a category of research heretofore largely neglected in ICT-related disciplines (particularly in business and information systems engineering, BISE, as well as information systems research, ISR), and argue that future research investigating human-machine interactions should consider the neurobiological perspective as a valuable complement to traditional concepts.