Social media is an amazing platform for enhancing public exposure. Anyone, even social bots, can reach out to a vast community and expose one's opinion. But what happens when fake news is (un)intentionally spread within a social media? This paper reviews techniques that can be used to fabricate fake news and depicts a scenario where social bots evolve in a fully semantic Web to infest social media with automatically generated deceptive information.
CCS CONCEPTS• Information systems → World Wide Web; Social networks; Internet communications tools; • Human-centered computing → Social content sharing;
This paper presents a framework allowing emblematic gestures detection, segmentation and their recognition for human-robots interaction purposes. This framework is based on a new coding of arms’ kinematics reflecting both the muscular activity of the performer and the appearance of arm seen by the recipient when a gesture is performed. Following that, gestures can be seen as sequences of torques activations leading arm’s parts to express a comprehensive meaning. In addition, these sequences have very stable topologies and shapes regardless to performers. This facilitates the generalization of the recognition process with a minimalistic learning effort for online usages. Promising results were obtained for a set of 5 classes of gestures performed by 19 different persons.
Abstract. 3D upper body pose estimation is a topic greatly studied by the computer vision society because it is useful in a great number of applications, mainly for human robots interactions including communications with companion robots. However there is a challenging problem: the complexity of classical algorithms that increases exponentially with the dimension of the vectors' state becomes too difficult to handle. To tackle this problem, we propose a new approach that combines several annealing particle filters defined independently for each limb and belief propagation method to add geometrical constraints between individual filters. Experimental results on a real human gestures sequence will show that this combined approach leads to reliable results.
This article presents an algorithm for 3D upper body tracking. This algorithm is a combination of two well-known methods: annealing particle filter and belief propagation. It is worth to underline that the 3D body tracking presents a challenging problem because of the high dimensionality of state space and so because of the huge computational time.In this work, we show that with our algorithm, it is possible to tackle this problem. Experiments both on real and synthetic human gesture sequences demonstrate that this combined approach leads to reliable results, as it reduces computational time without loosing robustness.
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