SBA Research was founded in 2006 as the first Austrian research center for information security by the TU Wien, the Graz University of Technology, and the University of Vienna.
The organization consists of a group of highly educated and experienced researchers, the majority of whom have or are pursuing doctorates in theoretical and applied areas of information security. They contribute to the active scientific community by publishing peer-reviewed articles at prestigious security conferences and in peer-reviewed publications. Many of the team have earned important honors and are well-known globally for their scientific contributions.
Many of the folks at SBA Research give seminars on the technical and organizational elements of information security at their Austrian partner institutions and Universities of Applied Sciences as part of their academic careers. They devote special emphasis to sharing research results and making students aware of the newest advancements in information security while teaching the future generation of specialists in crucial elements of information security.
International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE) 2022
CD-MAKE is a joint effort of IFIP TC 5 (Information Technology Applications), TC 12 (Artificial Intelligence), IFIP WG 8.4 (E-Business: Multidisciplinary research and practice), IFIP WG 8.9 (Enterprise Information Systems), and IFIP WG 12.9 (Computational Intelligence) and is held in conjunction with the International Conference on Availability, Reliability, and Security (ARES).
CD is for Cross-Domain and refers to the integration and evaluation of several subjects and application domains (e.g., health AI, Industry 4.0, etc.) in order to develop diverse ideas and viewpoints. The conference is devoted to providing an international forum for new ideas and a fresh look at approaches for putting wild ideas into business for the benefit of people. Serendipity is a desired result that will allow techniques to cross-fertilize and algorithmic discoveries to be transferred.
Machine Learning & Knowledge Extraction (MAKE) is an acronym for Machine Learning & Knowledge Extraction.
Machine learning is concerned with gaining a better understanding of intelligence in order to create algorithms that can learn from data and improve over time. “The artificial production of knowledge from experience,” according to the original definition. The task at hand is to find meaningful structural and/or temporal patterns (“knowledge”) in such data, which is frequently concealed and inaccessible to humans. Machine learning is the fastest-growing technological discipline today, with a wide range of applications in fields such as health, Industry 4.0, recommender systems, voice recognition, autonomous driving, and so on.
The problem is making decisions under uncertainty, and probabilistic inference has had a huge impact on AI and statistical learning. We may use the inverse probability to infer unknowns, learn from data, and create predictions to help us make better decisions. The increasingly complicated data sets, whether in social networks, recommender systems, health, or Industry 4.0 applications, necessitate effective, relevant, and usable solutions for knowledge discovery and extraction.
The CD-MAKE conference’s purpose is to function as a catalyst by bringing together scholars from these seven fields in a cross-disciplinary setting, stimulating new ideas, and encouraging multi-disciplinary problem-solving.