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The effect of personality and gender on individuals' propensity for corruption and corrupt behavior
(2020)
A Systemtic Literature Review of Practical Virtual and Augmented Reality Solutions in Surgery
(2020)
Linking Intrusion Detection System Information and System Model to Redesign Security Architecture
(2020)
Hierarchy-aware Learning of Sequential Tool Usage via Semi-automatically Constructed Taxonomies
(2020)
Learning for E-Learning
(2020)
Design Intelligence - Pitfalls and Challenges When Designing AI Algorithms in B2B Factory Automation
(2020)
Can one 3D print a laser?
(2020)
A Systematic Literature Review of Medical Chatbot Research from a Behavior Change Perspective
(2020)
The Future Approach to Simplify the Cloud-Service Market Using a Standardized Description Language
(2020)
Self-Management of Diabetes Mellitus Patients Using mHealth Applications: A Systematic Review
(2020)
Cluster-clean-label: an interactive machine learning approach for labeling high-dimensional data
(2020)
Two-Level Classification of Chronic Stress Using Machine Learning on Resting-State EEG Recordings
(2020)
A Context and Augmented Reality BPMN and BPMS Extension for Industrial Internet of Things Processes
(2022)
In the context of Industry 4.0, smart factories enable a new level of highly individualized and very efficient production, driven by highly automated processes and connected Industrial Internet of Things (IIoT) devices. Yet the IIoT process context, crucial for operational process enactment, cannot be readily represented in processes as currently modeled. Despite automation progress, manual tasks performed by humans (such as maintenance) remain, and while complicated tasks can be supported by Augmented Reality (AR) devices, they remain insufficiently integrated into global production processes. To seamlessly integrate process automation, IIoT context, and AR, this paper contributes BPMN-CARX, a Context and Augmented Reality eXtension (CARX) for BPMN (Business Process Model and Notation) and the CARX Framework, which enables AR and IIoT context integration with existing Business Process Management Systems (BPMSs). An Industry 4.0 case study demonstrates its feasibility and applicability.
Leveraging Augmented Reality to Support Context-Aware Tasks in Alignment with Business Processes
(2021)
The seamless inclusion of Augmented Reality (AR) with Business Process Management Systems (BPMSs) for Smart Factory and Industry 4.0 processes remains a challenge. Towards this end, this paper contributes an approach integrating context-aware AR into intelligent business processes to support and guide manufacturing personnel tasks and enable live task assignment optimization and support task execution quality. Our realization extends two BPMSs (Camunda and AristaFlow) and various AR devices. Various AR capabilities are demonstrated via a simulated industrial case study.
DEKXTROSE: An Education 4.0 Mobile Learning Approach and Object-Aware App Based on a Knowledge Nexus
(2020)
The exponential growth in knowledge coupled with the decreasing knowledge half-life creates a challenging situation for educational programs - particularly those preparing software engineers for their very dynamic high-technology field. Teachers in high technology education areas are challenged in selecting and making relevant knowledge intuitively accessible to students, especially with regard the highly dynamic digital and software technologies. This paper contributes a knowledge nexus-based multimedia approach aligned with Higher Education 4.0 for creating learning apps on mobile devices that support multiple didactic models, leverage intrinsic curiosity and motivation, support gamification, and enable digital collaboration. Object recognition is used to trigger learning paths, and various didactic methods are supported via workflow-like learning flows to support group or team-based learning. A prototype app was realized to demonstrate its feasibility and an empirical evaluation in software engineering shows the didactic potential and advantages of the approach, which can be readily generalized and applied to the arts, sciences, etc.
Software models in the Unified Modeling Language (UML) can been created or automatically reverse-engineered and used for quickly gaining structural insights into larger, legacy, or unfamiliar software. But as the size, structural complexity, and interdependencies between software components in larger systems grows, two-dimensional viewing and modeling has limitations, and new ways of visualizing larger models and numerous associated diagrams of different types are needed to intuitively convey structural and relational insights. To investigate the feasibility of using Virtual Reality (VR) to create an immersive UML-based software modeling experience, this paper contributes a VR solution concept for visualizing, navigating, modeling, and interacting with software models using UML notation. An implementation shows its feasibility while an empirical evaluation highlights its potential.
Production processes in Industry 4.0 settings are usually highly automated. However, many complicated tasks, such as machine maintenance, must be executed by human workers. In current smart factories, such tasks can be supported by Augmented Reality (AR) devices. These AR tasks rely on high numbers of contextual factors like live data from machines or work safety conditions and are mostly not well integrated into the global production process. This can lead to various problems like suboptimal task assignment, over-exposure of workers to hazards like noise or heat, or delays in the production process. Current Business Process Management (BPM) Systems (BPMS) are not capable of readily taking such factors into account. There- fore, this contribution proposes a novel approach for context- integrated modeling and execution of processes with AR tasks. Our practical evaluations show that our AR Process Framework can be easily integrated with prevalent BPMS. Furthermore, we have created a comprehensive simulation scenario and our findings suggest that the application of this system can lead to various benefits, like better quality of AR task execution and cost savings regarding the overall Industry 4.0 processes.
Industry 4.0 production comprises complicated highly automated processes. However, human activities are also a crucial component of these processes, e.g., for machine main- tenance. Task assignment of human resources in this domain is challenging, as many factors have to be taken into account to ensure effective and efficient activity execution and satisfy special conditions (like worker safety). To overcome the limita- tions of current Business Process Management (BPM) Systems regarding activity resource assignment, this contribution provides a BPM-integrated approach that applies fuzzy sets for activity assignment. Our findings suggest that this approach can be easily applied to complex production scenarios, while providing efficient performance even with a large number of concurrent activity assignment requests. Additionally, our evaluation shows its potential for improved work distribution which can lead to cost savings in Industry 4.0 production processes.
Additive manufacturing of optical elements out of polymer allow new design concepts for optics. The parts are built up layer by layer. Unlike polymer binding with glass particles with its sintering process no secondary step is necessary for polymer printing to create the final part. With more and more printers and transparent materials available, this technology becomes more and more relevant for prototyping or custom optics. Therefor a deep understanding of the optical effects in the part is desirable. Key property of optical elements is the refractive index. The materials for polymer printing are most commonly resins that cure under UV-exposure and show lower refractive indices in liquid phase than cured. Assuming a dependency of the refractive index on the grade of polymerization and therefor the UV-exposure, the layering process of additive manufacturing causes variations of the refractive index within the part. Using the Scanning Focused Refractive Index Microscopy, the distribution of the refractive index within and between the layers is analyzed. The analysis includes comparisons between raw parts after printing and parts after UV post curing. Additionally, layer free samples from a Continuous Liquid Interface Printing System are examined for the homogeneity of the refractive index distribution. The purpose of the presentation is to give a detailed insight into the optical effects occurring at the layer interfaces of elements created by additive manufacturing. Possible use cases of the refractive index distributions within the part are also discussed.