Aviv Segev

John Pomerat, Aviv Segev

Deep learning has been successful in the theoretical aspect. For deep learning to succeed in industry, we need to have algorithms capable of handling many inconsistencies appearing in real data. These inconsistencies can have large effects on the implementation of a deep learning algorithm. Artificial Intelligence is currently changing the medical industry. However, receiving authorization to use medical data for training machine learning algorithms is a huge hurdle. A possible solution is sharing the data without sharing the patient information. We propose a multi-party computation protocol for the deep learning algorithm. The protocol enables to conserve both the privacy and the security of the training data. Three approaches of neural networks assembly are analyzed: transfer learning, average ensemble learning, and series network learning. The results are compared to approaches based on data-sharing in different experiments. We analyze the security issues of the proposed protocol. Although the analysis is based on medical data, the results of multi-party computation of machine learning training are theoretical and can be implemented in multiple research areas.

Jung-Hun Kim, Aviv Segev

The large volume of scientific publications is likely to have hidden knowledge that can be used for suggesting new research topics. We propose an automatic method that is helpful for generating research hypotheses in the field of physics using the massive number of physics journal publications. We convert the text data of titles and abstract sections in publications to a bipartite graph, extracting words of physical matter composed of chemical elements and extracting related keywords in the paper. The proposed method predicts the formation of new links between matter and keyword nodes based on collaborative filtering and matter popularity. The formation of links represents research hypotheses, as it suggests the new possible relationships between physical matter and keywords for physical properties or phenomena. The suggested method has better performance than existing methods for link prediction in the entire bipartite graph and the subgraph that contains only a specific keyword, such as `antiferromagnetism' or `superconductivity.'