In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Ex Input:
The paper analyzes the interaction between humans and computers in terms of response time in solving the image-based CAPTCHA. In particular, the analysis focuses on the attitude of the different Internet users in easily solving four different types of image-based CAPTCHAs which include facial expressions like: animated character, old woman, surprised face, worried face. To pursue this goal, an experiment is realized involving 100 Internet users in solving the four types of CAPTCHAs, differentiated by age, Internet experience, and education level. The response times are collected for each user. Then, association rules are extracted from user data, for evaluating the dependence of the response time in solving the CAPTCHA from age, education level and experience in internet usage by statistical analysis. The results implicitly capture the users’ psychological states showing in what states the users are more sensible. It reveals to be a novelty and a meaningful analysis in the state-of-the-art.

Ex Output:
Analysis of the Human-Computer Interaction on the Example of Image-based CAPTCHA by Association Rule Mining


Ex Input:
There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learn­ ing causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concern­ ing how the presence of one cause of an effect makes an­ other cause less probable) support the hypothesis that humans reach the same judgments as algorithms for do­ ing inference in Bayesian networks. Next, it is shown how studies by Piaget indicate that humans learn causal structure by observing the same independencies and de­ pendencies as those used by certain algorithms for learn­ ing the structure of a Bayesian network. Based on this indication, a subjective definition of causality is for­ warded. Finally, methods for further testing the accu­ racy of these claims are discussed.

Ex Output:
THE CoGNITIVE PROCESSING OF CAUSAL KNOWLEDGE


Ex Input:
Contextual bandit learning is an increasingly popular approach to optimizing recommender systems via user feedback, but can be slow to converge in practice due to the need for exploring a large feature space. In this paper, we propose a coarse-to-fine hierarchical approach for encoding prior knowledge that drastically reduces the amount of exploration required. Intuitively, user preferences can be reasonably embedded in a coarse low-dimensional feature space that can be explored efficiently, requiring exploration in the high-dimensional space only as necessary. We introduce a bandit algorithm that explores within this coarse-to-fine spectrum, and prove performance guarantees that depend on how well the coarse space captures the user’s preferences. We demonstrate substantial improvement over conventional bandit algorithms through extensive simulation as well as a live user study in the setting of personalized news recommendation.

Ex Output:

Hierarchical Exploration for Accelerating Contextual Bandits