Bausch, David; Krämer, Tobias; Mauroner, Oliver (2024)
International Journal of Innovation and Technology Management.
DOI: 10.1142/S0219877024500299
In the face of increasing digitization, companies must make significant changes to their offerings and operations to remain competitive. This digital transformation of organizations includes a digital transformation of the workplace, which is often met with resistance from employees. While it is recognized that reducing employee resistance is crucial for organizations, there is a limited understanding of the antecedents of employee resistance in the context of digital transformation, different resistance behaviors, and potential countermeasures. Drawing on technostress and employee resistance theories, we address these research gaps. Results from two empirical studies support our central prediction that digital transformation of the workplace causes technostress, which in turn promotes passive and active resistance behaviors among employees. Additionally, we highlight that organizations can use digital literacy facilitation to reduce employee technostress and resistance.
Finn, Markus (2024)
Medizinrecht (MedR) 42 (3), S. 208-212.
DOI: 10.1007/s00350-024-6704-0
Müller-Czygan, Günter (2024)
Prof. Eduard Babulak (Hrsg.). Advances in Digital Transformation. 2024.
Wagener, Andreas (2024)
HAC Investmentkonferenz 2024, Hamburg 2024.
Peinl, René; Wirth, Johannes (2024)
International Journal on Natural Language Computing (IJNLC) 2024 (1).
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering in German and English language, which requires models to provide elaborate answers without external document retrieval (RAG). The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best English MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. The best German model also surpasses ChatGPT for the equivalent dataset. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.
Molenda, Paul; Moreno-Garibaldi, Pablo; Alvarez-Vera, Melvyn; Beltrán-Fernández, Juan Alfonso; Carrera-Espinoza, Rafael; Manuel Hdz-García, Héctor; Díaz-Guillen, J. C.; Muñoz-Arroyo, Rita; Ortega, Javier A. (2024)
Molenda, Paul; Moreno-Garibaldi, Pablo; Alvarez-Vera, Melvyn...
Journal of Manufacturing and Materials Processing 2024 (8).
DOI: 10.3390/jmmp8020048
Wolff, Dietmar (2024)
Altenpflege-online.net 03.2024 2024, S. 56-58.
Wolff, Dietmar; Eckhardt, Thordis; Klingbeil, Darren (2024)
Interview mit D. Wolff, T. Eckhardt veröffentlicht in: D. Klingbeil: „Bei der Pflege erleben wir eine hohe Aufgeschlossenheit“, Häusliche Pflege plus, erschienen am 26.02.2024 2024.
Buchmann, Thomas; Fraas, Jonas (2024)
Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering, MODELSWARD 2024, Rome, Italy, S. 227-234.
DOI: 10.5220/0012421900003645
Class diagrams are at the core of object oriented modeling. They are the foundation of model-driven software engineering and backed up by a wide range of supporting tools. In most cases, source code may be generated from class diagrams which results in increasing productivity of developers. In this paper we present an approach that allows the automatic conversion of hand-drawn sketches of class diagrams into corresponding UML models and thus can help to speed up the development process significantly.
Eichler, Christian; Röckl, Jonas; Jung, Benedikt; Schlenk, Ralph; Müller, Tilo; Hönig, Timo (2024)
Eichler, Christian; Röckl, Jonas; Jung, Benedikt; Schlenk, Ralph; Müller, Tilo...
Design Automation for Embedded Systems 2024.
DOI: 10.1007/s10617-024-09283-1
Large-scale attacks on IoT and edge computing devices pose a significant threat. As a prominent example, Mirai is an IoT botnet with 600,000 infected devices around the globe, capable of conducting effective and targeted DDoS attacks on (critical) infrastructure. Driven by the substantial impacts of attacks, manufacturers and system integrators propose Trusted Execution Environments (TEEs) that have gained significant importance recently. TEEs offer an execution environment to run small portions of code isolated from the rest of the system, even if the operating system is compromised. In this publication, we examine TEEs in the context of system monitoring and introduce the Trusted Monitor (TM), a novel anomaly detection system that runs within a TEE. The TM continuously profiles the system using hardware performance counters and utilizes an application-specific machine-learning model for anomaly detection. In our evaluation, we demonstrate that the TM accurately classifies 86% of 183 tested workloads, with an overhead of less than 2%. Notably, we show that a real-world kernel-level rootkit has observable effects on performance counters, allowing the TM to detect it. Major parts of the TM are implemented in the Rust programming language, eliminating common security-critical programming errors.
Stark, Oliver; Dölz, Michael; Kluck, Johannes; Plessing, Tobias (2024)
RET.Con Tagungsband 2024, S. 115-128.
Leuoth, Sebastian (2024)
DUZ Wissenschaft & Management Ausgabe 01.2024 2024 (1), S. 19-21.
Wolff, Dietmar (2024)
care konkret 2024 (5), S. 6.
Prelog, Martina; Jeske, Samuel D.; Asam, Claudia; Fuchs, André; Wieser, Andreas; Gall, Christine; Wytopil, Monika; Müller-Schmucker, Sandra M.; Beileke, Stephanie; Goekkaya, Mehmet; Kling, Elisabeth; Geldmacher, Christof; Rubio-Acero, Raquel; Plank, Michael; Christa, Catharina; Willmann, Annika; Vu, Martin; Einhauser, Sebastian; Weps, Manuela; Lampl, Benedikt M.J.; Almanzar, Giovanni; Kousha, Kimia; Schwägerl, Valeria; Liebl, Bernhard; Weber, Beatrix; Drescher, Johannes; Scheidt, Jörg; Siebenhaar, Yannic; Reinel, Dirk; Wogenstein, Florian; Gefeller, Olaf; Messmann, Helmut; Protzer, Ulrike; Liese, Johannes; Hoelscher, Michael; Wagner, Ralf; Steininger, Philipp; Überla, Klaus; Covako-Study, Group (2024)
Prelog, Martina; Jeske, Samuel D.; Asam, Claudia; Fuchs, André; Wieser, Andreas...
Journal of Clinical Virology 170, 105622.
DOI: 10.1016/j.jcv.2023.105622
Wolff, Dietmar (2024)
Leitungskräfte Akademie 2024 (2), S. 42-50.
Wolff, Dietmar (2024)
SOZIALwirtschaft aktuell 2024, S. 5 .
Weber, Beatrix; Achenbach, Marcus (2024)
ECPPM 2024.
Achenbach, Marcus; Weber, Beatrix; Rivas, Paul (2024)
ECPPM 2024.
Hamann, K. (2024)
Interview mit D. Wolff veröffentlicht in: K. Hamann: „Haben DiGA keinen Nutzen?“, care konkret 3/19.01.2024 2024 (3), S. 3 .
Molenda, Paul; Groneberg, Hajo; Schötz, Sebastian; Döpper, Frank (2024)
Procedia CIRP, 56th CIRP International Conference on Manufacturing Systems 2023 2023, 2212-8271 (120), S. 189-194.
DOI: 10.1016/j.procir.2023.08.034
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