0000000000735965
AUTHOR
Andrii Gontarenko
Anonymization as homeomorphic data space transformation for privacy-preserving deep learning
Industry 4.0 is largely data-driven nowadays. Owners of the data, on the one hand, want to get added value from the data by using remote artificial intelligence tools as services, on the other hand, they concern on privacy of their data within external premises. Ideal solution for this challenge would be such anonymization of the data, which makes the data safe in remote servers and, at the same time, leaves the opportunity for the machine learning algorithms to capture useful patterns from the data. In this paper, we take the problem of supervised machine learning with deep feedforward neural nets and provide an anonymization algorithm (based on the homeomorphic data space transformation),…
A Mobile Healthcare System for Sub-saharan Africa
The disparity between healthcare systems in developed countries and underdeveloped countries is huge, particularly due to the fact that the healthcare infrastructure of former is based on a sophisticated technological infrastructure. Efforts are being made worldwide to bridge this disparity and make healthcare services affordable even to the most remote areas of undeveloped countries. Recent growth of mobile networks in underdeveloped countries argues for building mHealth systems and applications on their basis. However, peculiarities of the area introduce difficulties into potential use cases of mobile devices, thus making the copying of mHealth services from developed countries inapplicab…
Cloning and training collective intelligence with generative adversarial networks
Industry 4.0 and highly automated critical infrastructure can be seen as cyber‐physical‐social systems controlled by the Collective Intelligence. Such systems are essential for the functioning of the society and economy. On one hand, they have flexible infrastructure of heterogeneous systems and assets. On the other hand, they are social systems, which include collaborating humans and artificial decision makers. Such (human plus machine) resources must be pre‐trained to perform their mission with high efficiency. Both human and machine learning approaches must be bridged to enable such training. The importance of these systems requires the anticipation of the potential and previously unknow…