The authors propose a video-summarisation method based on visual and categorical diversities using pre-trained deep visual and categorical models. Their method extracts visual and categorical features from a pre-trained deep convolutional network (DCN) and a pre-trained word-embedding matrix. Using visual and categorical information they obtain a video diversity estimation, which is used as an importance score to select segments from the input video that best describes it. Their method also allows performing queries during the search process, in this way personalising the resulting video summaries according to the particular intended purposes. The performance of the method is evaluated using different pre-trained DCN models in order to select the architecture with the best throughput. They then compare it with other state-of-the-art proposals in video summarisation using a data-driven approach with the public dataset SumMe, which contains annotated videos with perfragment importance. The results show that their method outperforms other proposals in most of the examples. As an additional advantage, their method requires a simple and direct implementation that does not require a training stage.

The number of AI applications in education is growing every day. One recent AI application in the educational sector is Chatbot technology, which is used to support teaching and administrative tasks. This document presents the design and implementation of a Chatbot called Tashi-Bot that helps applicants and university students to obtain information from an educational institution about certain academic and administrative processes. Among these are processes related to well-being, tuition, costs, admission, and other services. In order to design the Chatbot, an analysis of the state of the art, methodologies, and suitable tools was carried out, and a survey was conducted to discover the needs of users and their preferences in the use of a Chatbot for this specific purpose. Tashi-Bot was implemented on the SnatchBot platform and later deployed on a Telegram channel. In its evaluation, a final survey was carried out to check on the satisfaction of the users. The results suggest that Tashi-Bot could help applicants and university students to find information on academic and administrative processes with great certainty and without the need for human interaction. Tashi-Bot can be found at: https://web.telegram.org/#/im?p=@TashiE_Bot..