Graphical novels such as comics and mangas are well known all over the world. The digital transition started to change the way people are reading comics, more and more on smartphones and tablets and less and less on paper. In the recent years, a wide variety of research about comics has been proposed and might change the way comics are created, distributed and read in future years. Early work focuses on low level document image analysis: indeed comic books are complex, they contains text, drawings, balloon, panels, onomatopoeia, etc. Different fields of computer science covered research about user interaction and content generation such as multimedia, artificial intelligence, humancomputer interaction, etc. with different sets of values. We propose in this paper to review the previous research about comics in computer science, to state what have been done and to give some insights about the main outlooks.
The general public tends to avoid reliable sources such as scientific literature due to their complex language and lacking background knowledge. Instead, they rely on shallow and derived sources on the web and in social media -often published for commercial or political incentives, rather than the informational value. Can text simplification help to remove some of these access barriers? This paper presents the CLEF 2023 SimpleText track tackling technical and evaluation challenges of scientific information access for a general audience. We provide appropriate reusable data and benchmarks for scientific text simplification, and promote novel research to reduce barriers in understanding complex texts. Our overall use-case is to create a simplified summary of multiple scientific documents based on a popular science query which provides a user with an accessible overview on this specific topic. The track has the following three concrete tasks. Task 1 (What is in, or out? ): selecting passages to include in a simplified summary. Task 2 (What is unclear? ): difficult concept identification and explanation. Task 3 (Rewrite this! ): text simplification -rewriting scientific text. The three tasks together form a pipeline of a scientific text simplification system.
EXplainable AI (XAI) was created to address the issue of Machine Learning's lack o transparency. Its methods are expanding, as are the ways of evaluating them, including human performance-based evaluations of explanations. These evaluations allow us to quantify the contribution of XAI algorithms to human decision-making. This work performs accuracy and response time measurements to evaluate SHAP explanations on an e-sports prediction task. The results of this pilot experiment contradict our intuitions about the beneficial potential of these explanations and allow us to discuss the difficulties of this evaluation methodology.
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