FakeNED: A Deep Learning Based-System for Fake News Detection from Social Media

Social networks are increasingly present in our daily life. They allow us to remain in contact with friends regardless of distances, to share posts, images, videos, to be part of communities or come across articles and news. Everything develops so quickly that formality and content are no longer given too much importance. Therefore, it is through the development of these social networks that the need to distinguish fake news from real ones has developed. In this paper, we propose FakeNED a system for the detection of fake news on social networks. It comprises a multimodal Deep Learning (DL) approach of extracting features both from the text and from the images of the article. For the first extraction, we implemented a BERT-based (Bidirectional Encoder Representations from Transformers) method with an initial pre-trained phase followed by a fine-tuning final phase. For the latter, we used a VGG-16 to develop the image feature extraction. The extracted features were then given as input to a Fully Connected Layer in order to obtain the final output. We conduct our experiments on Fakeddit dataset through which we obtained a result which outperforms the state-of-art models. Moreover, FakeNED includes a service for allowing users to easily estimate the truth of social media content

MONstEr: A Deep Learning-Based System for the Automatic Generation of Gaming Assets

In recent years, we have witnessed the spread of computer graphics techniques, used as a background map for movies and video games. Nevertheless, when creating 3D models with conventional computer graphics software, it is necessary for the user to manually change the placement and size. This requires expertise of computer graphics architecture and operations, which is time demanding. Applying Artificial Intelligence (AI) to games is currently an established research field. Starting from such premises, in this paper MONstEr (dEEp lEArNiNG GENErAtiON AssEt) a system for the automatic generation of virtual asset for videogames is presented. MONstEr exploits the principle of Deep Learning (DL) and in particular Generative models to automatically design new assets for videogames. The DL pipeline is the core of this system and it is based on a Deep Convolutional Generative Adversarial Network followed by Pixel2Mesh architecture for the 3D models generation. The approach was applied and tested on a newly collected dataset of images, "GameAssetDataset" which comprises characters representation extracted thanks a web crawler algorithm specifically developed for its acquisition. MONstEr expedites the implementation of solutions for new gamining environments, requiring only a small intervention in the 3D construction to insert the object in the game scene