ORCID Profile
0000-0003-2998-742X
Current Organisations
Universität Wien
,
Ferdinand Porsche FernFH
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Publisher: ACM
Date: 04-12-2017
Publisher: World Scientific Pub Co Pte Ltd
Date: 03-2019
DOI: 10.1142/S146902681950007X
Abstract: Computation Intelligence has inspired many researchers to develop the capability of computers to learn and solve a complex task in real-world problems. In this work, we propose an Artificial Bee Colony (ABC) to deal with the Stock Selection problem. We apply a Sigmoid-based Discrete-Continuous model with ABC to select appropriate features for stock scoring. The empirical study tests the performance of ABC compared with Genetic Algorithm (GA) and Differential Evolution (DE) algorithm by using data from the Stock Exchange Thailand. The empirical results show that the novel model stock selection significantly outperforms in terms of both investment return, ersity and model robustness.
Publisher: IGI Global
Date: 22-02-2019
DOI: 10.4018/978-1-5225-7847-5.CH010
Abstract: This chapter presents an implementation of a cybersecurity education program. The program aims to address some issues identified in current cybersecurity teaching in higher education on a European level, like the fragmentation of cybersecurity expertise or resource shortage, resulting in few higher education institutions to offer full degree programs. As a result of the Erasmus+ strategic partnership project SecTech, the program tries to overcome those issues by introducing collaborative development to cybersecurity education. SecTech lays the foundations for a collaborative education program, like the definition of a clear content, module and delivery structure, and the appropriate tool support to facilitate collaboration and content reuse. Additional effort is required to achieve long-term success, including the creation of a community that drives the content creation and maintenance, as well as an independent governance structure to steer the project in the long-term. While the project focuses on European collaboration, a global community is envisioned.
Publisher: MDPI AG
Date: 11-04-2022
DOI: 10.3390/MAKE4020016
Abstract: The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages, such as SPARQL, can support rich log analyses by leveraging semantic relationships between objects in heterogeneous log streams. Constructing, materializing, and maintaining centralized log knowledge graphs, however, poses significant challenges. To tackle this issue, we propose VloGraph—a distributed and virtualized alternative to centralized log knowledge graph construction. The proposed approach does not involve any a priori parsing, aggregation, and processing of log data, but dynamically constructs a virtual log KG from heterogeneous raw log sources across multiple hosts. To explore the feasibility of this approach, we developed a prototype and demonstrate its applicability to three scenarios. Furthermore, we evaluate the approach in various experimental settings with multiple heterogeneous log sources and machines the encouraging results from this evaluation suggest that the approach can enable efficient graph-based ad-hoc log analyses in federated settings.
Publisher: Springer International Publishing
Date: 2016
Publisher: No publisher found
Date: 2016
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 12-2016
Publisher: ACM
Date: 02-12-2019
Publisher: SAGE Publications
Date: 09-09-2023
Publisher: Springer International Publishing
Date: 2018
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2018
Publisher: No publisher found
Date: 2018
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer International Publishing
Date: 2021
Publisher: ACM
Date: 09-10-2017
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 09-2019
Publisher: No publisher found
Date: 2019
Publisher: Springer International Publishing
Date: 2016
Publisher: Unpublished
Date: 2018
Publisher: ACM
Date: 10-12-2018
Publisher: No publisher found
Date: 2016
Location: Austria
Location: Austria
No related grants have been discovered for Gerald Quirchmayr.