CHALLENGES IN THE DEPLOYMENT AND OPERATION OF MACHINE LEARNING IN PRACTICE

Machine learning has recently emerged as a powerful technique to increase operational efficiency or to develop new value propositions. However, the translation of a prediction algorithm into an operationally usable machine learning model is a time-consuming and in various ways challenging task. In this work, we target to systematically elicit the challenges in deployment and operation to enable broader practical dissemination of machine learning applications. To this end, we first identify relevant challenges with a structured literature analysis. Subsequently, we conduct an interview study with machine learning practitioners across various industries, perform a qualitative content analysis, and identify challenges organized along three distinct categories as well as six overarching clusters. Eventually, results from both literature and interviews are evaluated with a comparative analysis. Key issues identified include automated strategies for data drift detection and handling, standardization of machine learning infrastructure, and appropriate communication and expectation management

GESCHÄFTSMODELLE 4.0: Baukasten zur Entwicklung datenbasierter Geschäftsmodelle

Die Digitalisierung ermöglich Unternehmen den Zugriff auf einen neuen Schatz an Ressourcen: Daten. Doch wie sind diese Daten wirtschaftlich zu nutzen? Das Praxishandbuch zeigt Ihnen, wie Sie datenbasierte Geschäftsmodelle entwickeln, um gezielt einen strategischen Wettbewerbsvorteil aufbauen zu können. Hierfür steht ein Baukasten aus methodischen Werkzeugen zur Verfügung, welcher Sie Schritt für Schritt durch die Entwicklung Ihres individuellen datenbasierten Geschäftsmodells führt

Prozessorentwicklung im ASIC-Design-Center

Am Institut für Angewandte Forschung wird seit Jahren eine Prozessorfamilie unter dem Kurznamen SIRIUS entwickelt, die ursprünglich ausschließlich für die Lehre gedacht und inzwischen eine beachtliche Leistungsfähigkeit erreicht hat

Non-parametric Camera-Based Calibration of Optical See-Through Glasses for AR Applications

This work describes a non-parametric camera-based method for the calibration of Optical See-Through Glasses (OSTG). Existing works model the optical system through perspective projection and parametric functions. In the border areas of the displays such models are often inadequate. Moreover, rigid calibration patterns, that produce only a small amount of non-equidistant point correspondences, are used. In order to overcome these disadvantages every single display pixel is calibrated individually. The error prone user interaction is avoided by using cameras placed behind the displays of the OSTG. The displays show a shifting pattern that is used to calculate the pixels' locations. A camera mounted rigidly on the OSTG is used to find the relations between the system components. The obtained results show better accuracies than in previous works and prove that a second calibration step for user adaptation is necessary for high accuracy applications

[Poster] Non-Parametric Camera-Based Calibration of Optical See-Through Glasses for Augmented Reality Applications

This work describes a camera-based method for the calibration of optical See-Through Glasses (STGs). A new calibration technique is introduced for calibrating every single display pixel of the STGs in order to overcome the disadvantages of a parametric model. A non-parametric model compared to the parametric one has the advantage that it can also map arbitrary distortions. The new generation of STGs using waveguide-based displays [5] will have higher arbitrary distortions due to the characteristics of their optics. First tests show better accuracies than in previous works. By using cameras which are placed behind the displays of the STGs, no error prone user interaction is necessary. It is shown that a high accuracy tracking device is not necessary for a good calibration. A camera mounted rigidly on the STGs is used to find the relations between the system components. Furthermore, this work elaborates on the necessity of a second subsequent calibration step which adapts the STGs to a specific user. First tests prove the theory that this subsequent step is necessary

Dental Students’ Oral Health-Related Quality of Life and Temporomandibular Dysfunction-Self-Rating versus Clinical Assessment

The aim of this study was to compare dental students’ self-perception of oral health with the results of a clinical examination of the masticatory system. Seventy-four dental students (38 (51.4%) females and 36 (48.6%) males) completed the Oral Health Impact Profile questionnaire (OHIP-G-14) and underwent a clinical examination according to the Graz Dysfunction Index (GDI). Data were analyzed with descriptive and comparative statistics. Median OHIP-G-14 scores were 3 (IQR 0–6) in the total collective, 4 (1–11) in females, and 2 (0–4) in males (p = 0.072). A score of 0 was found in 29.7% of the sample. The results of the GDI were 50% “normal function”, 43.2% “adaptation”, 5.4% “compensation”, and 1.4% “dysfunction”. The comparison of OHIP-G-14 scores and DGI groups showed a significant difference (p = 0.031). Based on the questionnaire, less than one third of the sample indicated maximum oral health-related quality of life. In contrast, the GDI revealed “normal function” or “adaptation” in 93.2%. Dental students underappreciated their oral health condition. Health assessments should not be solely questionnaire-based, especially in health professionals (-to-be). To establish a valid diagnosis of the state of health, self-assessment must be complemented by an objective clinical examination, e.g., GDI