This paper addresses exploratory search in large collections of historical texts. By way of example, we apply our method to a collection of documents comprising dossiers of the former East-German Ministry for State Security, and classical texts. The bases of our approach are topic models, a class of algorithms that define and infer themes pervading the corpus as probability distributions over t...

With the increasing capabilities of measurement devices and computing machines, the amount of recorded data grows rapidly. It is so high that manual processing is no longer feasible. The Visual Analytics approach is powerful because it combines the strengths of human recognition and vision system with today’s computing power. Different, but strongly linked visualizations and views provide uniqu...

In this study, the researcher chose three different vocabulary techniques (Visual Representation, Textual Enhancement, and Glossing) and compared them with traditional method of teaching vocabulary. 80 advanced EFL Learners were assigned as four intact groups (three experimental and one control group) through using a proficiency test and a vocabulary test as a pre-test. In the visual group, stu...

Bias detection in the computer vision field is a necessary task, to achieve fair models. These biases are usually due undesirable correlations present data and learned by model. Although explainability can be way gain insights into model behavior, reviewing explanations not straightforward. This work proposes methodology analyze without using explainability. By doing so, we reduce potential noi...

Over the last decade, Computer Vision, branch of Artificial Intelligence aimed at understanding visual world, has evolved from simply recognizing objects in images to describing pictures, answering questions about images, aiding robots maneuver around physical spaces, and even generating novel content. As these tasks applications have modernized, so too reliance on more data, either for model t...