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.
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Question: The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started following this practice. Although the wide-spread practice of clickbait makes the reader’s reliability on media vulnerable, a large scale analysis to reveal this fact is still absent. In this paper, we analyze 1.67 million Facebook posts created by 153 media organizations to understand the extent of clickbait practice, its impact and user engagement by using our own developed clickbait detection model. The model uses distributed sub-word embeddings learned from a large corpus. The accuracy of the model is 98.3%. Powered with this model, we further study the distribution of topics in clickbait and non-clickbait contents.

Answer: Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?


Question: Integration between biology and information science benefits both fields. Many related models have been proposed, such as computational visual cognition models, computational motor control models, integrations of both and so on. In general, the robustness and precision of recognition is one of the key problems for object recognition models. In this paper, inspired by features of human recognition process and their biological mechanisms, a new integrated and dynamic framework is proposed to mimic the semantic extraction, concept formation and feature re-selection in human visual processing. The main contributions of the proposed model are as follows: (1) Semantic feature extraction: Local semantic features are learnt from episodic features that are extracted from raw images through a deep neural network; (2) Integrated concept formation: Concepts are formed with local semantic information and structural information learnt through network. (3) Feature re-selection: When ambiguity is detected during recognition process, distinctive features according to the difference between ambiguous candidates are re-selected for recognition. Experimental results on hand-written digits and facial shape dataset show that, compared with other methods, the new proposed model exhibits higher robustness and precision for visual recognition, especially in the condition when input samples are smantic ambiguous. Meanwhile, the introduced biological mechanisms further strengthen the interaction between neuroscience and information science.

Answer: A Novel Biologically Mechanism-Based Visual Cognition Model –Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity


Question: <lb>We consider stochastic bandit problems with a continuous set of arms and where the expected re-<lb>ward is a continuous and unimodal function of the arm. No further assumption is made regarding<lb>the smoothness and the structure of the expected reward function. For these problems, we propose<lb>the Stochastic Pentachotomy (SP) algorithm, and derive finite-time upper bounds on its regret and<lb>optimization error. In particular, we show that, for any expected reward function μ that behaves as<lb>μ(x) = μ(x)− C|x− x| locally around its maximizer x for some ξ, C > 0, the SP algorithm is<lb>order-optimal. Namely its regret and optimization error scale as O(<lb>√<lb>T log(T )) and O(<lb>√<lb>log(T )/T ),<lb>respectively, when the time horizon T grows large. These scalings are achieved without the knowledge<lb>of ξ and C. Our algorithm is based on asymptotically optimal sequential statistical tests used to suc-<lb>cessively trim an interval that contains the best arm with high probability. To our knowledge, the SP<lb>algorithm constitutes the first sequential arm selection rule that achieves a regret and optimization error<lb>scaling as O(<lb>√<lb>T ) and O(1/<lb>√<lb>T ), respectively, up to a logarithmic factor for non-smooth expected<lb>reward functions, as well as for smooth functions with unknown smoothness.

Answer:
Unimodal Bandits without Smoothness