Refine
Year of publication
- 2018 (88) (remove)
Document Type
- Conference Proceeding (88) (remove)
Keywords
Institute
- Fakultät Bauingenieurwesen (1)
- Fakultät Informatik (13)
- Fakultät Maschinenbau (3)
- Fakultät Wirtschafts-, Kultur- und Rechtswissenschaften (2)
- Institut für Angewandte Forschung - IAF (3)
- Institut für Optische Systeme - IOS (9)
- Institut für Strategische Innovation und Technologiemanagement - IST (5)
- Institut für Systemdynamik - ISD (6)
- Institut für professionelles Schreiben - IPS (2)
- Konstanz Institut für Corporate Governance - KICG (2)
- Konstanzer Institut für Prozesssteuerung - KIPS (2)
Deep neural networks have become a veritable alternative to classic speaker recognition and clustering methods in recent years. However, while the speech signal clearly is a time series, and despite the body of literature on the benefits of prosodic (suprasegmental) features, identifying voices has usually not been approached with sequence learning methods. Only recently has a recurrent neural network (RNN) been successfully applied to this task, while the use of convolutional neural networks (CNNs) (that are not able to capture arbitrary time dependencies, unlike RNNs) still prevails. In this paper, we show the effectiveness of RNNs for speaker recognition by improving state of the art speaker clustering performance and robustness on the classic TIMIT benchmark. We provide arguments why RNNs are superior by experimentally showing a “sweet spot” of the segment length for successfully capturing prosodic information that has been theoretically predicted in previous work.
Research on Shadow IT is facing a conceptual dilemma in cases where previously "covert" systems developed by business entities (individual users, business workgroups, or business units) are integrated in the organizational IT management. These systems become visible, are therefore not "in the shadows" anymore, and subsequently do not fit to existing definitions of Shadow IT. Practice shows that some information systems share characteristics of Shadow IT, but are created openly in alignment with the IT department. This paper therefore proposes the term "Business-managed IT" to describe "overt" information systems developed or managed by business entities. We distinguish Business-managed IT from Shadow IT by illustrating case vignettes. Accordingly, our contribution is to suggest a concept and its delineation against other concepts. In this way, IS researchers interested in IT originated from or maintained by business entities can construct theories with a wider scope of application that are at the same time more specific to practical problems. In addition, value-laden terminology is complemented by a vocabulary that values potentially innovative developments by business entities more adequately. From a practical point of view, the distinction can be used to discuss the distribution of task responsibilities for information systems.
This paper describes the effectiveness and efficiency of Virtual Reality training during a commissioning process. Therefore, 500 picking orders with more than 2000 part-picking operations with 30 test persons have been conducted and analyzed in the Modellfabrik Bodensee. The study points out the advantages and disadvantages of virtual training in comparison to a real execution of a picking process with and without any training.
The paper investigates an innovative actuator combination based on the magnetic shape memory technology. The actuator is composed of an electromagnet, which is activated to produce motion, and a magnetic shape memory element, which is used passively to yield multistability, i.e. the possibility of holding a position without input power. Based on the experimental open-loop frequency characterization of the actuator, a position controller is developed and tested in several experiments.