Dongjae Kim

Dongjae Kim, Minseok Choi

This paper explores caching of fractions of a video content, not caching of an entire content, to increase the expected video quality. We first show that the highest-quality content is better to be cached and propose the caching policy of video chunks having different qualities. Our caching policy utilizes the characteristics of video contents that video files can be encoded into multiple versions with different qualities, each file consists of many chunks, and chunks can have different qualities. Extensive performance evaluations are conducted to show that caching of content fractions, rather than an entire content, can improve the expected video quality especially when the channel conditions is sufficiently good to cooperate with nearby BS or helpers.

Dongjae Kim, Jong-Kook Kim

The skip-gram (SG) model learns word representation by predicting the words surrounding a center word from unstructured text data. However, not all words in the context window contribute to the meaning of the center word. For example, less relevant words could be in the context window, hindering the SG model from learning a better quality representation. In this paper, we propose an enhanced version of the SG that leverages context information to produce word representation. The proposed model, Contextual Skip-gram, is designed to predict contextual words with both the center words and the context information. This simple idea helps to reduce the impact of irrelevant words on the training process, thus enhancing the final performance