One of the fundamental research challenges in network science is centrality analysis, i.e., identifying the nodes that play the most important roles in the network. In this article, we focus on the game-theoretic approach to centrality analysis. While various centrality indices have been recently proposed based on this approach, it is still unknown how general is the game-theoretic approach to centrality and what distinguishes some game-theoretic centralities from others. In this article, we attempt to answer this question by providing the first axiomatic characterization of game-theoretic centralities. Specifically, we show that every possible centrality measure can be obtained following the game-theoretic approach. Furthermore, we study three natural classes of game-theoretic centrality, and prove that they can be characterized by certain intuitive properties pertaining to the well-known notion of Fairness due to Myerson.

Over the past years, distributed representations have proven effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey is focused on semantic representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their main limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and provides an analysis of five important aspects: interpretability, sense granularity, adaptability to different domains, compositionality and integration into downstream applications.

The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains. In this paper, we study the problem of classifying social media posts during a crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data from past similar events (e.g., Flood) and unlabeled data for the current event. We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single unified deep learning framework. Our experiments with two real-world crisis datasets collected from Twitter demonstrate significant improvements over several baselines.

In this study, we investigate how a robot can generate novel and creative actions from its own experience of learning basic actions. Inspired by a machine learning approach to computational creativity, we propose a dynamic neural network model that can learn and generate robot's actions. We conducted a set of simulation experiments with a humanoid robot. The results showed that the proposed model was able to learn the basic actions and also to generate novel actions by modulating and combining those learned actions. The analysis on the neural activities illustrated that the ability to generate creative actions emerged from the model's nonlinear memory structure self-organized during training. The results also showed that the different way of learning the basic actions induced the self-organization of the memory structure with the different characteristics, resulting in the generation of different levels of creative actions. Our approach can be utilized in human-robot interaction in which a user can interactively explore the robot's memory to control its behavior and also discover other novel actions. If the robot is only capable of reproducing the behaviors that it has learned, the user might easily lose his/her interests in the interaction with the robot. In addition, it is cumbersome for the user to teach every single behavior of the robot.

Japanese developer Inti Creates has lifted the lid on Bloodstained: Curse of the Moon at this year's BitSummit -- an annual, Kyoto-based celebration of the finest indie games. Curse of the Moon is a classic 8-bit follow-up to Bloodstained: Ritual of the Night, originally promised as a Kickstarter reward if certain stretch goals were met. Fans ensured that they were, injecting over $5 million into the project and helping it earn the title of second most-funded video game of all time. In Curse of the Moon's launch trailer, producer Koji Igarashi said that backers who pledged a particular amount should already have access to codes, which can be retrieved via the official home page or a private Kickstarter update. Igarashi, typically flamboyant in his cowboy hat, introduced Curse of the Moon's basic gameplay, characters, and unmistakably retro aesthetic.

The three-day expo opened here on Thursday, attracting more than 200 exhibitors from ten countries and regions, with some 1,000 AI products on show. Visitors watch a concept unmanned vehicle at the Global AI Product Application Expo 2018, in Suzhou of east China's Jiangsu Province, May 10, 2018. The three-day expo opened here on Thursday, attracting more than 200 exhibitors from ten countries and regions, with some 1,000 AI products on show. The three-day expo opened here on Thursday, attracting more than 200 exhibitors from ten countries and regions, with some 1,000 AI products on show. The three-day expo opened here on Thursday, attracting more than 200 exhibitors from ten countries and regions, with some 1,000 AI products on show.

A Tesla sedan with a semi-autonomous Autopilot feature rear-ended a fire department truck at 60 mph (97 kph) apparently without braking before impact, but police say it's unknown if the Autopilot feature was engaged. The cause of the Friday evening crash, involving a Tesla Model S and a fire department mechanic truck stopped at a red light, was under investigation, said police in South Jordan, a suburb of Salt Lake City. The crash, in which the Tesla driver was injured, comes as federal safety agencies investigate the performance of Tesla's semi-autonomous driving system. The Tesla's air bags were activated in the crash, South Jordan police Sgt. The Tesla's driver suffered a broken right ankle, and the driver of the Unified Fire Authority mechanic truck didn't require treatment, Winkler said.

This blogpost was co-authored by Mark Latonero, PhD, Data & Society Research Lead, Data & Human Rights and Melanie Penagos, Data & Society Research Analyst, Data & Human Rights. The first blogpost in a series on Artificial Intelligence and Human Rights, it summarizes a multidisciplinary workshop held at Data & Society on April 26 and 27, 2018. Multiple sectors of our global society are grappling to make sense of how AI may transform or alter the way we live, work, and relate to one another and our institutions. At the same time, "Artificial Intelligence" is a slippery and highly contextual concept -- the way a mathematician defines AI can diverge significantly from a marketing executive or a causal reader of science fiction. This tension makes discussions about norms that could shape or regulate AI systems a thoroughly contested and challenging space.

The fast-emerging field of Artificial Intelligence, which has suddenly caught the attention of the IT industry and the governments across the world, is facing a large skills shortage, a top Microsoft official has said. The Artificial Intelligence (AI) is also facing the challenge of appropriate use of data, group programme manager of Microsoft Learning Matt Winkler told PTI. "There is a pretty large skills shortage. Lots of folks are talking about it (AI). A lot of folks are very, very excited about it and then they want to go and make that real. And when they go to make that real, there's a really large skills shortage," Winkler said.

WASHINGTON: The fast-emerging field of Artificial Intelligence, which has suddenly caught the attention of the IT industry and the governments across the world, is facing a large skills shortage, a top Microsoft official has said. The Artificial Intelligence (AI) is also facing the challenge of appropriate use of data, group programme manager of Microsoft Learning Matt Winkler told . "There is a pretty large skills shortage. Lots of folks are talking about it (AI). A lot of folks are very, very excited about it and then they want to go and make that real. And when they go to make that real, there's a really large skills shortage," Winkler said.