458 research outputs found
Detecting frames in news headlines and its application to analyzing news framing trends surrounding U.S. gun violence
Different news articles about the same topic often offer a variety of perspectives: an article written about gun violence might emphasize gun control, while another might promote 2nd Amendment rights, and yet a third might focus on mental health issues. In communication research, these different perspectives are known as “frames”, which, when used in news media will influence the opinion of their readers in multiple ways. In this paper, we present a method for effectively detecting frames in news headlines. Our training and performance evaluation is based on a new dataset of news
headlines related to the issue of gun violence in the United States. This Gun Violence Frame
Corpus (GVFC) was curated and annotated by
journalism and communication experts. Our
proposed approach sets a new state-of-the-art
performance for multiclass news frame detection, significantly outperforming a recent baseline by 35.9% absolute difference in accuracy. We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U.S. between 2016 and 2018.Published versio
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
While deep reinforcement learning (RL) methods have achieved unprecedented
successes in a range of challenging problems, their applicability has been
mainly limited to simulation or game domains due to the high sample complexity
of the trial-and-error learning process. However, real-world robotic
applications often need a data-efficient learning process with safety-critical
constraints. In this paper, we consider the challenging problem of learning
unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire
a strategy that combines perception and control, we represent the policy by a
convolutional neural network. We develop a hierarchical approach that combines
a model-free policy gradient method with a conventional feedback
proportional-integral-derivative (PID) controller to enable stable learning
without catastrophic failure. The neural network is trained by a combination of
supervised learning from raw images and reinforcement learning from games of
self-play. We show that the proposed approach can learn a target following
policy in a simulator efficiently and the learned behavior can be successfully
transferred to the DJI quadrotor platform for real-world UAV control
Cold-start Sequential Recommendation via Meta Learner
This paper explores meta-learning in sequential recommendation to alleviate
the item cold-start problem. Sequential recommendation aims to capture user's
dynamic preferences based on historical behavior sequences and acts as a key
component of most online recommendation scenarios. However, most previous
methods have trouble recommending cold-start items, which are prevalent in
those scenarios. As there is generally no side information in the setting of
sequential recommendation task, previous cold-start methods could not be
applied when only user-item interactions are available. Thus, we propose a
Meta-learning-based Cold-Start Sequential Recommendation Framework, namely
Mecos, to mitigate the item cold-start problem in sequential recommendation.
This task is non-trivial as it targets at an important problem in a novel and
challenging context. Mecos effectively extracts user preference from limited
interactions and learns to match the target cold-start item with the potential
user. Besides, our framework can be painlessly integrated with neural
network-based models. Extensive experiments conducted on three real-world
datasets verify the superiority of Mecos, with the average improvement up to
99%, 91%, and 70% in HR@10 over state-of-the-art baseline methods.Comment: Accepted at AAAI 202
The Impact Mechanism of Pro-Environmental Behaviours
Based on the theory of relationship quality, the research constructed the impact mechanism model of pro-environmental behaviours by applying tourist’s perceived value as the antecedent variable, while relationship quality (satisfaction and loyalty) was treated as the mediators, and place attachment as a moderator. Surveys were conducted at the Daweishan National Forest Park in Hunan Province, China, with 674 valid questionnaires collected. The empirical results provided evidence that the direct effect of perceived value of tourists on tourists’ pro-environmental behaviours is significantly positive, and the indirect effect of relationship quality (satisfaction and loyalty) between perceived value of tourists. Recommendations and strategies for further research and implementing tourist stratification management are suggested
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