This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challenge focused on classifying whether posts from Twitter and Reddit support, deny, query, or comment a hidden rumour, truthfulness of which is the topic of an underlying discussion thread. We formulate the problem as a stance classification, determining the rumour stance of a post with respect to the previous thread post and the source thread post. The recent BERT architecture was employed to build an end-to-end system which has reached the F1 score of 61.67% on the provided test data. It finished at the 2nd place in the competition, without any hand-crafted features, only 0.2% behind the winner.

That is, if you were fortunate enough to get an invite. While last year's invite-only conference, held in southern California's Palm Springs, produced striking images of Bezos strolling with a robotic dog designed by Boston Dynamics, the CEO this time took to the stage with a flying robo-dragonfly. Much of this year's buzz, however, has come straight from the stars; among the attendees is actor Mark Hamill, who portrayed'Star Wars' protagonists'Luke Skywalker' in the films' original trilogy. Bezos demonstrated a robotic dragon fly on stage that circled around his head. As Hamill, who recently revived Skywalker for the latest iteration of the Star Wars franchise, mingled with guests, the conference's other attendees showcased their newest and most exciting revelations in the fields of robotics, artificial intelligence, machine learning, and more.

The rise of non-linear and interactive media such as video games has increased the need for automatic movement animation generation. In this survey, we review and analyze different aspects of building automatic movement generation systems using machine learning techniques and motion capture data. We cover topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods. We conclude by presenting a discussion of the reviewed literature and outlining the research gaps and remaining challenges for future work.

I personally love what Victor Frankenstein attempts (although it meets a terrible end) but I must admit how scary this era of technological development is. Humans are forging ahead in the field of artificial intelligence, quickly replacing manpower as we switch from manual to automatic for a number of tasks. Robots are already powering our homes, working in our labs, picking our songs and now, they will be directing our movies. SEE ALSO: This machine writes poetry and it might be better than you at it! Up next, artificial intelligence is all set to take over major roles in filmmaking.

Farhad Bulbul is with the Department of Mathematics, Jessore University of Science and Technology, Bangladesh (email: farhad@just.edu.bd). Saiful Islam is with the Department of Mathematics, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Bangladesh. Dr. Hazrat Ali is with the Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Pakistan (email: hazratali@cuiatd.edu.pk). Abstract-- In this paper, we present an approach for identification of actions within depth action videos. First, we process the video to get motion history images (MHIs) and static history images (SHIs) corresponding to an action video based on the use of 3D Motion Trail Model (3DMTM). We then characterize the action video by extracting the Gradient Local Auto-Correlations (GLAC) features from the SHIs and the MHIs. The two sets of features i.e., GLAC features from MHIs and GLAC features from SHIs are concatenated to obtain a representation vector for action. Finally, we perform the classification on all the action samples by using the l2-regularized Collaborative Representation Classifier (l2-CRC) to recognize different human actions in an effective way. We perform evaluation of the proposed method on three action datasets, MSR-Action3D, DHA and UTD-MHAD. Through experimental results, we observe that the proposed method performs superior to other approaches. I. INTRODUCTION Research in human action recognition (HAR) is considered as one of the most interesting domains of computer vision. The action recognition system is being extensively applied in human security system, medical science, social awareness, and entertainment [1], [2], [3], [4].. Indeed, to develop an applicable action recognition system, researchers still need to win against the odds due to diversity in human body sizes, appearances, postures, motions, clothing, camera motions, viewing angles, and illumination. In the early stage, the human action recognition system was developed by researchers based on RGB data [5], [6], [7], [8].

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information. In general, the attributes are not isolated but connected with each other, which forms a knowledge graph (KG). In this paper, we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG. To automatically discover both high-order structure information and semantic information of the KG, we sample from the neighbors for each entity in the KG as their receptive field, then combine neighborhood information with bias when calculating the representation of a given entity. The receptive field can be extended to multiple hops away to model high-order proximity information and capture users' potential long-distance interests. Moreover, we implement the proposed KGCN in a minibatch fashion, which enables our model to operate on large datasets and KGs. We apply the proposed model to three datasets about movie, book, and music recommendation, and experiment results demonstrate that our approach outperforms strong recommender baselines.

Not if David Hanson has anything to do with it. The founder of Hanson Robotics, David has been dreaming of making sentient robots since childhood and while, for many of us, our childhood dreams never come true (I'm still waiting for my invite to become the sixth Spice Girl), Hanson's endless drive to push the field of robotics and AI further has meant that we're closer than ever to awakening consciousness in machines. To put it into context, Hanson is the mastermind behind the eerily lifelike robot Sophia, who uses sophisticated algorithms to track faces and engage in conversations with emotion. Quick witted enough to make Piers Morgan laugh on Good Morning Britain and win a game of rock, paper, scissors on The Tonight Show Starring Jimmy Fallon, Sophia's ability to socialise with humans might freak you out, but you can't deny it's impressive. In 2017, she also became the first robot to be granted citizenship to any country.

WASHINGTON/SAN, FRANCISCO - Some of the biggest names in media and tech are gearing up to move into streaming in what could be a major challenge to market leader Netflix. Apple is expected to make its move with an announcement March 25 on its media plans, with a war chest estimated at some $1 billion and partners including stars like Jennifer Aniston and director J.J. Abrams involved in content. Walt Disney Co. has announced its new streaming service Disney will launch this year, as will another from WarnerMedia, the newly acquired media-entertainment division of AT&T. The new entrants, with more expected, could launch a formidable challenge to Netflix, which has some 140 million paid subscribers in 190 markets, and to other services such as Amazon and Hulu. "It's really going to change the industry," said Alan Wolk, co-founder of the consulting firm TVREV who follows the sector.

She discussed how to use artificial intelligence for enhanced interaction, communication, and greater understanding.

Artificial intelligence (AI) has come into the mainstream in ways many people don't consciously realize.