WhenT is larger, more recent edges are assignedsmallDAmagnitudes,sothattheessentialsemantic information is preserved. This theorem guarantees that our DA techiniques do not break the original edge time distribution. There are 4,066 drop-out events (= 0.98%). Based on the validation results, using two TGAT layers and two attention heads with dropout rate of 0.1 gives the best performance. For inference, we inductively compute the embeddings for both the unseen and observed nodes at each time point that the graph evolves, or when the node labels are updated.

Campaigners from Stop Image-Based Abuse delivered a petition to Downing Street calling for greater protection against deepfake image abuse. Campaigners from Stop Image-Based Abuse delivered a petition to Downing Street calling for greater protection against deepfake image abuse. Victims of deepfake image abuse have called for stronger protection against AI-generated explicit images, as the law criminalising the creation of non-consensual intimate images comes into effect. Campaigners from Stop Image-Based Abuse delivered a petition to Downing Street with more than 73,000 signatures, urging the government to introduce civil routes to justice such as takedown orders for abusive imagery on platforms and devices. "Today's a really momentous day," said Jodie, a victim of deepfake abuse who uses a pseudonym.

Charlotte Owen: 'The Lords were blown away by these brilliant women.' Charlotte Owen: 'The Lords were blown away by these brilliant women.' 'I don't take no for an answer': how a small group of women changed the law on deepfake porn For Jodie*, watching the conviction of her best friend, and knowing she helped secure it, felt at first like a kind of victory. It was certainly more than most survivors of deepfake image-based abuse could expect. They had met as students and bonded over their shared love of music. In the years since graduation, he'd also become her support system, the friend she reached for each time she learned that her images and personal details had been posted online without her consent.

It began with an anonymous email. "I'm genuinely so, so sorry to reach out to you," it read. Beneath the words were three links to an internet forum. "Huge trigger warning … They contain lewd photoshopped images of you." Jodie (not her real name) froze.

In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or visualizations. However, as shown in cognitive science as well as in early AI research, concept understanding can also be improved by the alignment of a given instance for a concept with a similar counterexample. Contrasting a given instance with a structurally similar example which does not belong to the concept highlights what characteristics are necessary for concept membership. Such near misses have been proposed by Winston (1970) as efficient guidance for learning in relational domains. We introduce an explanation generation algorithm for relational concepts learned with Inductive Logic Programming (\textsc{GeNME}). The algorithm identifies near miss examples from a given set of instances and ranks these examples by their degree of closeness to a specific positive instance. A modified rule which covers the near miss but not the original instance is given as an explanation. We illustrate \textsc{GeNME} with the well known family domain consisting of kinship relations, the visual relational Winston arches domain and a real-world domain dealing with file management. We also present a psychological experiment comparing human preferences of rule-based, example-based, and near miss explanations in the family and the arches domains.

Modeling a sequence of interactions between users and items (e.g., products, posts, or courses) is crucial in domains such as e-commerce, social networking, and education to predict future interactions. Representation learning presents an attractive solution to model the dynamic evolution of user and item properties, where each user/item can be embedded in a euclidean space and its evolution can be modeled by dynamic changes in embedding. However, existing embedding methods either generate static embeddings, treat users and items independently, or are not scalable. Here we present JODIE, a coupled recurrent model to jointly learn the dynamic embeddings of users and items from a sequence of user-item interactions. JODIE has three components. First, the update component updates the user and item embedding from each interaction using their previous embeddings with the two mutually-recursive Recurrent Neural Networks. Second, a novel projection component is trained to forecast the embedding of users at any future time. Finally, the prediction component directly predicts the embedding of the item in a future interaction. For models that learn from a sequence of interactions, traditional training data batching cannot be done due to complex user-user dependencies. Therefore, we present a novel batching algorithm called t-Batch that generates time-consistent batches of training data that can run in parallel, giving massive speed-up. We conduct six experiments on two prediction tasks---future interaction prediction and state change prediction---using four real-world datasets. We show that JODIE outperforms six state-of-the-art algorithms in these tasks by up to 22.4%. Moreover, we show that JODIE is highly scalable and up to 9.2x faster than comparable models. As an additional experiment, we illustrate that JODIE can predict student drop-out from courses five interactions in advance.

Stranger Things, a show so continually obsessed over that it's become a game of find that reference, is still fascinating fans. The thriller pays tribute to a ton of iconic shows and movies, sometimes in ways that make the audience feel rewarded and cool, and other times in ways that make people feel like conspiracy theorists tumbling down an endless rabbit hole filled with mystery waffles. Of all the roughly 8,362 different influences in the canon of influences that the creators confirmed inspired the series, the PlayStation game Beyond Two Souls starring Ellen Page isn't one of them. But the interactive drama adventure game and the Netflix series do share some common elements. It's not that the show is directly referencing this game.