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Two-Level Classification of Chronic Stress Using Machine Learning on Resting-State EEG Recordings
(2020)
Cluster-clean-label: an interactive machine learning approach for labeling high-dimensional data
(2020)
Online Monitoring System for Photovoltaic Systems Using Anomaly Detection with Machine Learning
(2019)
Self-Management of Diabetes Mellitus Patients Using mHealth Applications: A Systematic Review
(2020)
High-performance exclusion of schizophrenia using a novel machine learning method on EEG data
(2019)
The Future Approach to Simplify the Cloud-Service Market Using a Standardized Description Language
(2020)
Digital Aura
(2004)
Early Detection of Alcohol Use Disorder Based on a Novel Machine Learning Approach Using EEG Data
(2021)
With the advent of modern communication media over the last decades, such as email, video conferencing, or instant messaging, a plethora of research has emerged that analyzes the association between communication media and negotiation processes and outcomes. This chapter reviews theoretical vantage points on communication media and negotiation and summarizes empirical findings from the last five decades. Specifically, the author focuses on media richness theory and the task/media fit hypothesis, grounding in communication, and media synchronicity theory as communication theoretical foundations that found traction in negotiation research. These theoretical vantage points are supplemented by a review of specific theoretical psychological aspects of communication media, the barrier effect and psychological distance theory. In the second part of the chapter, empirical evidence on communication media and negotiation is presented, derived from an extensive literature search of relevant peer-reviewed articles. The emphasis in this review of the empirical literature is on the communication medium as an independent variable. In other words, the author analyzes effects of communication media on the negotiation process (descriptive process parameters, economic reference points, negotiation behavior/tactics, individual psychological variables, assessment of the opponent) as well as economic (agreement, individual profit, joint profit, equality of agreement) and socio-emotional (satisfaction, future interaction, trust) outcomes. A succeeding subsection is devoted to communication medium choice in negotiation, a topic much less researched. The conclusion sums up the findings and sketches out some avenues for future research.
A Systematic Literature Review of Medical Chatbot Research from a Behavior Change Perspective
(2020)
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.
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.
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.
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.