Hyoungshick Kim

TL;DR: A simple but powerful model, the time-ordered graph, is presented, which reduces a dynamic network to a static network with directed flows, which enables it to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case.

TL;DR: A new image encryption algorithm using a large pseudorandom permutation which is combinatorially generated from small permutation matrices based on chaotic maps to provide comparable security with that of the conventional image encryption schemes based on Baker map or Logistic map.

TL;DR: This work is the first attempt to provide empirical comparisons of FL and SplitNN in real-world IoT settings in terms of learning performance and device implementation overhead and demonstrates that neither FL or SplitNN can be applied to a heavy model, e.g., with several million parameters, on resource-constrained IoT devices because its training cost would be too expensive for such devices.

TL;DR: The experimental results showed that the best-performing classification method - which uses the number of Twitter messages referring to a particular political party - achieved about 86% classification accuracy without any training phase.

TL;DR: An incremental and practical classification method which uses the number of Twitter messages referring to a particular political party or retweets, and a classifier leveraging the valuable semantic content of the List feature to estimate the overall political leaning of users is developed.