Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Janiesch, P."'
The term "generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data. The widespread diffusion of this technology with examples such as Dall-E 2
Externí odkaz:
http://arxiv.org/abs/2309.07930
Publikováno v:
IEEE Access, vol. 10, pp. 96492-96513 (2022)
In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication. It has become possible to distill complex lingu
Externí odkaz:
http://arxiv.org/abs/2211.14591
Publikováno v:
International Journal of Information Management, 2002, 102538
Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. Machine learning models with higher performance are often
Externí odkaz:
http://arxiv.org/abs/2206.10610
Autor:
Magdalena Wischnewski, Nicole Krämer, Christian Janiesch, Emmanuel Müller, Theodor Schnitzler, Carina Newen
Publikováno v:
Human-Machine Communication Journal, Vol 8, Pp 141-162 (2024)
Trust certification through so-called trust seals is a common strategy to help users ascertain the trustworthiness of a system. In this study, we examined trust seals for AI systems from two perspectives: (1) In a pre-registered online study particip
Externí odkaz:
https://doaj.org/article/402f3a484435494bbb8899df0fb85ea4
Publikováno v:
ACM SIGMIS Database: the DATABASE for Advances in Information Systems, Volume 54, Issue 1, 2023
Fueled by increasing data availability and the rise of technological advances for data processing and communication, business analytics is a key driver for smart manufacturing. However, due to the multitude of different local advances as well as its
Externí odkaz:
http://arxiv.org/abs/2110.06124
Publikováno v:
Business Process Management Journal, 2019, Vol. 25 No. 6, pp. 1273-1290
Changes in workflow relevant data of business processes at run-time can hinder their completion or impact their profitability as they have been instantiated under different circumstances. The purpose of this paper is to propose a context engine to en
Externí odkaz:
http://arxiv.org/abs/2110.04061
Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-ho
Externí odkaz:
http://arxiv.org/abs/2104.08885
Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model bui
Externí odkaz:
http://arxiv.org/abs/2104.05314
Digital transformation forces companies to rethink their processes to meet current customer needs. Business Process Management (BPM) can provide the means to structure and tackle this change. However, most approaches to BPM face restrictions on the n
Externí odkaz:
http://arxiv.org/abs/2011.13188
Publikováno v:
Inf Syst E-Bus Manage 17, 159-194 (2019)
Driven by digitization in society and industry, automating behavior in an autonomous way substantially alters industrial value chains in the smart service world. As processes are enhanced with sensor and actuator technology, they become digitally int
Externí odkaz:
http://arxiv.org/abs/2011.09239