Camera model identification refers to the problem of linking a picture to the camera model used to shoot it. As this might be an enabling factor in different forensic applications to single out possible suspects (e.g., detecting the author of child abuse or terrorist propaganda material), many accurate camera model attribution methods have been developed in the literature. One of their main drawbacks, however, is the typical closed-set assumption of the problem. This means that an investigated photograph is always assigned to one camera model within a set of known ones present during investigation, i.e., training time, and the fact that the picture can come from a completely unrelated camera model during actual testing is usually ignored. Under realistic conditions, it is not possible to assume that every picture under analysis belongs to one of the available camera models. To deal with this issue, in this paper, we present the first in-depth study on the possibility of solving the camera model identification problem in open-set scenarios. Given a photograph, we aim at detecting whether it comes from one of the known camera models of interest or from an unknown device. We compare different feature extraction algorithms and classifiers specially targeting open-set recognition. We also evaluate possible open-set training protocols that can be applied along with any open-set classifier. More specifically, we evaluate one training protocol targeted for open-set classifiers with deep features. We observe that a simpler version of those training protocols works with similar results to the one that requires extra data, which can be useful in many applications in which deep features are employed. Thorough testing on independent datasets shows that it is possible to leverage a recently proposed convolutional neural network as feature extractor paired with a properly trained open-set classifier...

Motivated by the need of solving machine learning problems over distributed datasets, we explore the use of coreset to reduce the communication overhead. Coreset is a summary of the original dataset in the form of a small weighted set in the same sample space. Compared to other data summaries, coreset has the advantage that it can be used as a proxy of the original dataset, potentially for different applications. However, existing coreset construction algorithms are each tailor-made for a specific machine learning problem. Thus, to solve different machine learning problems, one has to collect coresets of different types, defeating the purpose of saving communication overhead. We resolve this dilemma by developing coreset construction algorithms based on k-means/median clustering, that give a provably good approximation for a broad range of machine learning problems with sufficiently continuous cost functions. Through evaluations on diverse datasets and machine learning problems, we verify the robust performance of the proposed algorithms.

This paper describes the Speech Technology Center (STC) antispoofing systems submitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended version of the previous challenges and includes 2 evaluation conditions: logical access use-case scenario with speech synthesis and voice conversion attack types and physical access use-case scenario with replay attacks. During the challenge we developed anti-spoofing solutions for both scenarios. The proposed systems are implemented using deep learning approach and are based on different types of acoustic features. We enhanced Light CNN architecture previously considered by the authors for replay attacks detection and which performed high spoofing detection quality during the ASVspoof2017 challenge. In particular here we investigate the efficiency of angular margin based softmax activation for training robust deep Light CNN classifier to solve the mentioned-above tasks. Submitted systems achieved EER of 1.86% in logical access scenario and 0.54% in physical access scenario on the evaluation part of the Challenge corpora. High performance obtained for the unknown types of spoofing attacks demonstrates the stability of the offered approach in both evaluation conditions.

The first ever photo of a black hole has been revealed by scientists. The stunning image, showing a flaming ring of yellow and red, helped advance our understanding of the universe. Black holes are among the most massive and powerful phenomena in the known universe. But until now, we have not been able to see them because they drag in light. We'll tell you what's true.

Humanity has had its first ever peek at a black hole. The blazing image of a red and yellow circle is the first anyone has seen of the phenomenon. As well as giving an unprecedented view of one of the most powerful and mysterious things in the universe, it also helps develop our understanding of the fundamental forces that shape space. Scientists will be examining what exactly all the findings from this photo mean for years to come. And they expect to go on to discover even more, as they use the same techniques on other sources inside other galaxies.

AI can reveal how many cigarettes a person has smoked based on the DNA contained in a single drop of their blood, or scrutinize Islamic State propaganda to discover whether violent videos are radicalizing potential recruits. Because AI is such a powerful tool, Microsoft president Brad Smith told the crowd at Columbia University's recent Data Science Day that tech companies and universities performing AI research must also help ensure the ethical use of such technologies. AI is now an invisible but inextricable part of life for hundreds of millions of people. The rise of machine learning algorithms combined with cloud computing services has put massive computer power at the fingertips of companies and customers worldwide. These trends have also enabled the rise of data science that applies AI methods to constantly analyze information from online services and Internet-connected devices. In his talk, Smith emphasized the need for policies and laws that hold these systems and machines accountable to humans.

We naturally think of "intelligence" as a trait belonging to individuals. We're all--students, employees, soldiers, artists, athletes--regularly evaluated in terms of personal accomplishment, with "lone hero" narratives prevailing in accounts of scientific discovery, politics, and business. Similarly, artificial intelligence is typically defined as a quest to build individual machines that possess different forms of intelligence, even the kind of general intelligence measured in humans for more than a century. Yet focusing on individual intelligence, whether human or machine, can distract us from the true nature of accomplishment. As Thomas Malone, professor at MIT's Sloan School of Management and director of its Center for Collective Intelligence notes: "Almost everything we humans have ever done has been done not by lone individuals, but by groups of people working together, often across time and space." Malone, the author of 2004's The Future of Work and a pioneering researcher in the field of collective intelligence, is in a singular position to understand the potential of AI technologies to transform workers, workplaces, and societies. In this conversation with Deloitte's Jim Guszcza and Jeff Schwartz, he discusses a vision outlined in his recent book Superminds--a framework for achieving new forms of human-machine collective intelligence and its implications for the future of work. Can you tell us what a "supermind" is, and how you define collective intelligence? Thomas Malone, director, MIT Center for Collective Intelligence: A "supermind" is a group of individuals acting collectively in ways that seem intelligent, and collective intelligence essentially has the same definition. For many years, I defined collective intelligence as groups of individuals acting collectively in ways that seem intelligent. But I think it's probably more useful to think of collective intelligence as the property that a supermind has.

London (CNN Business)Social media faces a crisis of trust. Europe wants to make sure artificial intelligence doesn't go the same way. The European Commission on Monday unveiled ethics guidelines that are designed to influence the development of AI systems before they become deeply embedded in society. The intervention could help break the pattern of regulators being forced to play catch up with emerging technologies that lead to unanticipated negative consequences. The importance of doing so was underscored Monday when Britain proposed new rules that would make internet companies legally responsible for ridding their platforms of harmful content.

As NASA faces pressure to get astronauts to the Moon and considers human exploration of Mars, it will need to sort out a few major details -- like how to keep extraterrestrial habitats functioning even when there aren't any human occupants. To do this, NASA selected two new, university-led Space Technology Research Institutes (STRIs) and tasked them with developing automated Smart Habitats, or SmartHabs. One, the Habitats Optimized for Missions of Exploration (HOME), will develop autonomous systems, machine learning, robotic maintenance and onboard manufacturing for autonomous and self-maintained smart habitats. The HOME team includes researchers from the University of California, Davis; University of Colorado Boulder, Carnegie Mellon University, the Georgia Institute of Technology, Howard University, Texas A&M University and the University of Southern California -- as well as Sierra Nevada Corporation, Blue Origin and United Technology Aerospace Systems. The other STRI, Resilient ExtraTerrestrial Habitats institute (RETHi), will focus on SmartHabs that use autonomous robotics to adapt and recover from disruptions.

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.