Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure.
Human anctivity recognition systems from static images or video sequences are becoming more and more present in our life. Most computer vision applications such as human-computer interaction, virtual reality, public security, smart home monitoring, or autonomous robotics, to name a few, highly rely on human anctivity recognition. Of course, basic human activities, such as "walking" and "running", are relatively easy to recognize. On the other hand, identifying more complex activities is still a challenging task that could be solved by retrieving contextual information from the scene, such as objects, events, or concepts. Indeed, a careful analysis of the scene can help to recognize human activities taking place. In this work, we address a holistic video understanding task to provide a complete semantic level description of the scene. Our solution can bring significant improvements in human anctivity recognition tasks. Besides, it may allow equipping a robotic and autonomous system with contextual knowledge of the environment. In particular, we want to show how this vision module can be integrated into a social robot to build a more natural and realistic context-based Human-Robot Interaction. We think that social robots must be aware of the surrounding environment to react in a proper and socially acceptable way, according to the different scenarios.
In spacecraft health management a large number of time series is acquired and used for on-board units surveillance and for historical data analysis. The early detection of abnormal behaviors in telemetry data can prevent failures in the spacecraft equipment. In this paper we present an advanced monitoring system that was carried out in partnership with Thales Alenia Space Italia S.p.A, a leading industry in the field of spacecraft manufacturing. In particular, we developed an anomaly detection algorithm based on Generative Adversarial Networks, that thanks to their ability to model arbitrary distributions in high dimensional spaces, allow to capture complex anomalies avoiding the burden of hand crafted feature extraction. We applied this method to detect anomalies in telemetry data collected from a simulator of a Low Earth Orbit satellite. One of the strengths of the proposed approach is that it does not require any previous knowledge on the signal. This is particular useful in the context of anomaly detection where we do not have a model of the anomaly. Hence the only assumption we made is that an anomaly is a pattern that lives in a lower probability region of the data space.
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