EADTU's staff exchange programme on Artificial Intelligence in teaching and learning on the 29th of March at UOC in Barcelona has led to the …

Virtual humans need to be persuasive in order to promote behaviour change in human users. While several studies have focused on understanding the numerous aspects that influence the degree of persuasion, most of them are limited to dyadic interactions. In this paper, we present an evaluation study focused on understanding the effects of multiple agents on user's persuasion. Along with gender and status (authoritative & peer), we also look at type of focus employed by the agent i.e., user-directed where the agent aims to persuade by addressing the user directly and vicarious where the agent aims to persuade the user, who is an observer, indirectly by engaging another agent in the discussion. Participants were randomly assigned to one of the 12 conditions and presented with a persuasive message by one or several virtual agents. A questionnaire was used to measure perceived interpersonal attitude, credibility and persuasion. Results indicate that credibility positively affects persuasion. In general, multiple agent setting, irrespective of the focus, was more persuasive than single agent setting. Although, participants favored user-directed setting and reported it to be persuasive and had an increased level of trust in the agents, the actual change in persuasion score reflects that vicarious setting was the most effective in inducing behaviour change. In addition to this, the study also revealed that authoritative agents were the most persuasive.

Automatically generating animation from natural language text finds application in a number of areas e.g. movie script writing, instructional videos, and public safety. However, translating natural language text into animation is a challenging task. Existing text-to-animation systems can handle only very simple sentences, which limits their applications. In this paper, we develop a text-to-animation system which is capable of handling complex sentences. We achieve this by introducing a text simplification step into the process. Building on an existing animation generation system for screenwriting, we create a robust NLP pipeline to extract information from screenplays and map them to the system's knowledge base. We develop a set of linguistic transformation rules that simplify complex sentences. Information extracted from the simplified sentences is used to generate a rough storyboard and video depicting the text. Our sentence simplification module outperforms existing systems in terms of BLEU and SARI metrics.We further evaluated our system via a user study: 68 % participants believe that our system generates reasonable animation from input screenplays.

On the one hand, nowadays, fake news articles are easily propagated through various online media platforms and have become a grand threat to the trustworthiness of information. On the other hand, our understanding of the language of fake news is still minimal. Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured. Nevertheless, this has rarely been investigated in the fake news detection domain and faces tremendous challenges. First, existing methods for capturing discourse-level structure rely on annotated corpora which are not available for fake news datasets. Second, how to extract out useful information from such discovered structures is another challenge. To address these challenges, we propose Hierarchical Discourse-level Structure for Fake news detection. HDSF learns and constructs a discourse-level structure for fake/real news articles in an automated and data-driven manner. Moreover, we identify insightful structure-related properties, which can explain the discovered structures and boost our understating of fake news. Conducted experiments show the effectiveness of the proposed approach. Further structural analysis suggests that real and fake news present substantial differences in the hierarchical discourse-level structures.

Sonos and Ikea are teaming up for a new line of speakers. After previously announcing years ago that they would be working together, the two companies finally announced the first fruits of their partnership on Monday. Symfonisk, which translates to "symphonic" in Swedish, looks to bring Sonos' sound to Ikea's smart home plans. As with traditional Sonos speakers the new products will work with the Sonos app, be able to play music from the likes of Spotify, Apple Music and Pandora, and be able to connect to other Sonos-branded speakers that you may already have in your home. If you have two of the same speakers, i.e. two lamps, you can link them together to create a stereo pair.

Sonos and Ikea have teamed up to release a line of connected speakers that include a table lamp and a bookshelf. Both devices connect to the WiFi, can be controlled by an app and let users play music from their favorite service like Spotify or Apple Music. The lamp and bookshelf, part of a line called'Symfonisk,' are priced at $179 and $99, respectively, and are expected to become available for purchase in August. Sonos and Ikea have teamed up to release a line of connected speakers that include a table lamp and a bookshelf. While both devices are priced within the same affordable range typically found in Ikea products, they pack the high quality of any other Sonos speaker.

Netflix has stopped its users from sending videos to their Apple TVs from their phones. The shock decision removes one of the key features both from the Netflix apps and iPhones they are used on. Until now, it has been possible to send a Netflix video onto a TV using Apple's AirPlay, which allows people to cast the video from their phone to the TV. AirPlay is used mostly on Apple TVs at the moment, though Apple is in the process of rolling out the technology to other smart televisions. We'll tell you what's true.

Issuing large fines and hitting companies with bigger legal threats is taking a 20th century bullwhip approach to a problem that requires a nuanced solution,

Last week's breaking news story on The Robot Report was unfortunately the demise of Helen Greiner's company, CyPhy Works (d/b/a Aria Insights). The high-flying startup raised close to $40 million since its creation in 2008, making it the second business founded by an iRobot alum that has shuttered within five months. While it is not immediately clear why the tethered-drone company went bust, it does raise important questions about the long-term market opportunities for leashed robots. The tether concept is not exclusive to Greiner's company, there are a handful of drone companies that vie for marketshare, including: FotoKite, Elistair, and HoverFly. The primary driver towards chaining an Unmanned Ariel Vehicle (UAV) is bypassing the Federal Aviation Administration's (FAA) ban on beyond line of sight operations.

Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap, we propose to generate large-scale user/item interaction data sets by expanding pre-existing public data sets. Our key contribution is a technique that expands user/item incidence matrices matrices to large numbers of rows (users), columns (items), and non-zero values (interactions). The proposed method adapts Kronecker Graph Theory to preserve key higher order statistical properties such as the fat-tailed distribution of user engagements, item popularity, and singular value spectra of user/item interaction matrices. Preserving such properties is key to building large realistic synthetic data sets which in turn can be employed reliably to benchmark recommender systems and the systems employed to train them. We further apply our stochastic expansion algorithm to the binarized MovieLens 20M data set, which comprises 20M interactions between 27K movies and 138K users. The resulting expanded data set has 1.2B ratings, 2.2M users, and 855K items, which can be scaled up or down.