How to be successful on a video platform with 720K hours of video uploaded daily? This is the key question for every Youtube content creator. Especially, because there is only a thumbnail image and a short caption to attract the users’ attention and convince them to click a video. We introduce the Youtube-Science-Channels dataset containing more than 19k Youtube thumbnails and captions along with meta attributes, such as likes, dislikes and views from 40 channels. A Youtube video’s success is defined as generating lots of views. Given this labeling, a computational model is built in order to predict the success of a video using solely its thumbnail and caption. Experimental results are presented on both, the individual videos as well as aggregated per Youtube channel. Our combined vision and natural language model can accurately predict success. In addition, we analyse the effect of using clickbait in video captions. We show that clickbait is positively correlated with a Youtube channel’s success but that other factors such as high quality animations are important as well.

Man vs. machine competitions have always been attracting much public attention and the famous defeats of human champions in chess, Jeopardy!, Go or poker undoubtedly mark important milestones in the history of artificial intelligence. In this article we reflect on our experiences with a game-centric approach to teaching artificial intelligence that follows the historical development of algorithms by popping the hood of these champion bots. Moreover, we made available a server infrastructure for playing card games in perfect information and imperfect information playing mode, where students can evaluate their implementations of increasingly sophisticated game-playing algorithms in weekly online competitions, i.e. from rule-based systems to exhaustive and heuristic search in game trees to deep learning enhanced Monte Carlo methods and reinforcement learning completely freed of human domain knowledge. The evaluation of this particular course setting revealed enthusiastic feedback not only from students but also from the university authority. What started as an experiment became part of the standard computer science curriculum after just one implementation.

Hand eczema is a frequent dermatosis with severe health and financial consequences to patients and society. It follows a chronic course and persists up to 15 years after onset. Early detection of an exacerbation followed by the application of specific drugs for a few days can considerably reduce disease activity and avoid temporary disability. However, dermatitis patients usually rely on their own perception in assessing their skin condition and therefore often miss the time point for effective treatment. In this paper we present a prototype-based feasibility study of automated detection and quantification of hand eczema using texton-based imaging and machine-learning techniques.

Presents a novel, parameter-free technique for the segmentation and local description of line structures on multiple scales, both in 2D and in 3D. The algorithm is based on a nonlinear combination of linear filters and searches for elongated, symmetric line structures, while suppressing the response to edges. The filtering process creates one sharp maximum across the line-feature profile and across the scale-space. The multi-scale response reflects local contrast and is independent of the local width. The filter is steerable in both the orientation and scale domains, leading to an efficient, parameter-free implementation. A local description is obtained that describes the contrast, the position of the center-line, the width, the polarity, and the orientation of the line. Examples of images from different application domains demonstrate the generic nature of the line segmentation scheme. The 3D filtering is applied to magnetic resonance volume data in order to segment cerebral blood vessels.>