We also demonstrate the the detection of inflammatory, inauthentic, or otherwise ability to use this architecture to transfer knowledge nefarious communication. Character-level convolutional from labeled data in one domain to related neural networks (CNNs) are particularly well-suited for (supervised and unsupervised) tasks. Characterlevel this task--as opposed to a word-level model--because they neural networks and transfer learning are allow for non-vernacular discourse, misspelling, and other particularly valuable tools in the disinformation social media features (e.g., emoticons) to be learned without space because of the messy nature of social media, the constraint of fixed vocabularies (Zhang et al., 2015). We lack of labeled data, and the multi-channel tactics implement an adaptation of a neural network architecture of influence campaigns. We demonstrate their effectiveness recently demonstrated to be effective for text classification in several tasks relevant for detecting (Zhang et al., 2015; Józefowicz et al., 2016). The method disinformation: spam emails, review bombing, is purely content-based and does not require any additional political sentiment, and conversation clustering.

As video camera deployments continue to grow, the need to process large volumes of real-time data strains wide area network infrastructure. When per-camera bandwidth is limited, it is infeasible for applications such as traffic monitoring and pedestrian tracking to offload high-quality video streams to a datacenter. This paper presents FilterForward, a new edge-to-cloud system that enables datacenter-based applications to process content from thousands of cameras by installing lightweight edge filters that backhaul only relevant video frames. FilterForward introduces fast and expressive per-application "microclassifiers" that share computation to simultaneously detect dozens of events on computationally constrained edge nodes. Only matching events are transmitted to the cloud. Evaluation on two real-world camera feed datasets shows that FilterForward reduces bandwidth use by an order of magnitude while improving computational efficiency and event detection accuracy for challenging video content. This paper is an extended version of (Canel et al., 2019).

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges. However, these models focus on undirected graphs and therefore ignore the potential direction of the link, which is limiting for numerous real-life applications. In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs. We present a new gravity-inspired decoder scheme that can effectively reconstruct directed graphs from a node embedding. We empirically evaluate our method on three different directed link prediction tasks, for which standard graph AE and VAE perform poorly. We achieve competitive results on three real-world graphs, outperforming several popular baselines.

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When women are over-represented in the workforce, it tends be in industries of assistance – cleaning, nursing, secretarial work and, now, the world of virtual assistants. Research by Unesco has shown that using default female voices in AI – as Microsoft has done with Cortana, Amazon with Alexa, Google with Google Assistant and Apple with Siri – is furthering the belief that women exist merely to help men to get on with more important things. There is no real reason for AI technologies to be gendered at all, but we are at the mercy of tech companies "staffed by overwhelmingly male engineering teams", fixated on living out a Captain Kirk fantasy and delegating to the subservient, silky-voiced computers of Star Trek. These systems are unapologetically built by men, for men. They can even struggle to understand the "breathy" voices of women as software is often developed with male voice samples.

Want to add more video to your content mix? Have you considered repurposing your blog content into video? There's no need to spend hours recording video with an expensive camera when you have existing content and access to free tools. In this article, you'll learn how to use free tools to turn blog posts into videos you can share on social media. You can turn a blog post into a video pretty easily by building a slideshow presentation that communicates key ideas and converting that slideshow into a video file.

Welcome to EURACTIV's Digital Brief, your weekly update on all things digital in the EU. You can subscribe to the newsletter here. With the Brits and the Dutch heading to the polls today, the big news of the week is the story that Facebook has removed around 80 pages spreading fake news or using tactics aimed at unfairly influencing the polls. The takedowns came following a discovery by the human rights group Avaaz, in which it uncovered far-right disinformation networks in France, UK, Germany, Spain, Italy and Poland, posting content that was viewed an estimated 533 million times over the past three months. EURACTIV Digital went to investigate further and paid Avaaz a visit at their recently opened'Citizens' War Room' in Brussels (pictured below).

In the last 10 years, we've seen some significant breakthroughs in the domain of artificial intelligence (AI) and machine learning. In 2011, IBM Watson showed the world that it can be a reality TV show winner. In 2014, Google acquired an AI company called DeepMind, and one of its project, AlphaGo, beat the European Go champion in 2015. In 2016, Google made its TensorFlow library open source, which made machine learning accessible to the masses. Last year, people were left dumbfounded when Google Duplex made a haircut appointment over the phone.

Disinformation and Fake News are hardly anything new but the power of both is increasing exponentially because of the power of social media. Websites like Twitter and Facebook serve up information, images and events based on what they know about our likes, dislikes and desires, thereby supporting our prejudices and undermining open and tolerant debate. But Fake News is yesterday's news. Deep Fakes is where Fake News might be moving next and Deep Fakes could be have a bigger impact and even harder to spot, address or undermine. Deep Fakes is the use of deep learning – a branch of machine learning or artificial intelligence – to marry digital images with fake or forged audio files.

Deep learning is increasingly capable of assessing the emotion of human faces, looking across an image to estimate how happy or sad the people in it appear to be. What if this could be applied to television news, estimating the average emotion of all of the human faces seen on the news over the course of a week? While AI-based facial sentiment assessment is still very much an active area of research, an experiment using Google's cloud AI to analyze a week's worth of television news coverage from the Internet Archive's Television News Archive demonstrates that even within the limitations of today's tools, there is a lot of visual sentiment in television news. To better understand the facial emotion of television, CNN, MSNBC and Fox News and the morning and evening broadcasts of San Francisco affiliates KGO (ABC), KPIX (CBS), KNTV (NBC) and KQED (PBS) from April 15 to April 22, 2019, totaling 812 hours of television news, were analyzed using Google's Vision AI image understanding API with all of its features enabled, including facial detection. Facial detection is very different from facial recognition. It only counts that a human face is present in an image, it does not actually attempt to discern who that person is.