Recently, digital music libraries have been developed and can be plainly accessed. Latest research showed that current organization and retrieval of music tracks based on album information are inefficient. Moreover, they demonstrated that people use emotion tags for music tracks in order to search and retrieve them. In this paper, we discuss separability of a set of emotional labels, proposed in the categorical emotion expression, using Fisher's separation theorem. We determine a set of adjectives to tag music parts: happy, sad, relaxing, exciting, epic and thriller. Temporal, frequency and energy features have been extracted from the music parts. It could be seen that the maximum separability within the extracted features occurs between relaxing and epic music parts. Finally, we have trained a classifier using Support Vector Machines to automatically recognize and generate emotional labels for a music part. Accuracy for recognizing each label has been calculated; where the results show that epic music can be recognized more accurately (77.4%), comparing to the other types of music.

Afew years ago the cosmologist Max Tegmark found himself weeping outside the Science Museum in South Kensington. He'd just visited an exhibition that represented the growth in human knowledge, everything from Charles Babbage's difference engine to a replica of Apollo 11. What moved him to tears wasn't the spectacle of these iconic technologies but an epiphany they prompted. "It hit me like a brick," he recalls, "that every time we understood how something in nature worked, some aspect of ourselves, we made it obsolete. Once we understood how muscles worked we built much better muscles in the form of machines, and maybe when we understand how our brains work we'll build much better brains and become utterly obsolete." Tegmark's melancholy insight was not some idle hypothesis, but instead an intellectual challenge to himself at the dawn of the age of artificial intelligence. What will become of humanity, he was moved to ask, if we manage to create an intelligence that outstrips our own?

Apple finally announced the iPhone X this week, complete with a facial recognition system that Apple calls FaceID. Preliminary impressions are that FaceID will be difficult to trick, and should be secure for the average user, but researchers are eager to test its robustness. Consumer facial recognition has been around, but not yet at this scale, inviting questions about what its implications will be, particularly for privacy. Apple's new iOS 11 mobile operating system does have more crucial privacy protections against muggers and government officials alike but researchers detailed doubts this week about the "differential privacy" techniques Apple uses that are meant to aggregate and analyze customer data without invading their privacy. Over at the astounding, ongoing dumpster fire that is the Equifax data breach, Equifax admitted that hackers accessed its network through an Apache Struts web application vulnerability that had a patch available for two months before the initial intrusion.

Listen to Episode No. 174 of Slate Money Slate Plus members: Get your ad-free podcast feed. Felix Salmon of Fusion, political risk consultant Anna Szymanski, Slate Moneybox columnist Jordan Weissmann, and Cathy O'Neil, author of Weapons of Math Destruction, discuss: Check out other Panoply podcasts at panoply.fm. Felix Salmon is the editor of Cause & Effect. Anna Szymanski is an emerging markets expert and senior strategy officer at a political risk startup. Jordan Weissmann is Slate's senior business and economics correspondent.

Amid a raging global debate on the consequences of artificial intelligence (AI), India has formed a "policy group" to study the new technologies and recommend a framework for its adoption, IT industry body Nasscom said today. "We all are currently working out on a policy framework on AI," its vice president K S Viswanathan told PTI, when asked about concerns over AI or the intelligence exhibited by machines. He said a "policy group" has been created by the Ministry of Electronics and Information Technology with representation from the academia, which has done a lot of research on the subject, and Nasscom for the industry's perspective. The group will focus on aspects like skilling the workforce, privacy, security and fixing responsibility if anything goes wrong, Viswanathan said. "We have to create a thought leadership on what is this programme all about, what is the likely impact. Create a thought leadership when AI becomes a reality, what are the elements and sub-elements which need to be taken care of, how do we take care of that," he said.

Random survival forest can be extremely time consuming for large data set. In this paper we propose few computationally efficient algorithms in prediction of survival function. We explore the behavior of the algorithms for different cancer data sets. Our construction includes right censoring data too. We have also applied the same for competing risk survival function.

Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite number of loss functions. The present paper proposes a Riemannian stochastic quasi-Newton algorithm with variance reduction (R-SQN-VR). The key challenges of averaging, adding, and subtracting multiple gradients are addressed with notions of retraction and vector transport. We present convergence analyses of R-SQN-VR on both non-convex and retraction-convex functions under retraction and vector transport operators. The proposed algorithm is evaluated on the Karcher mean computation on the symmetric positive-definite manifold and the low-rank matrix completion on the Grassmann manifold. In all cases, the proposed algorithm outperforms the state-of-the-art Riemannian batch and stochastic gradient algorithms.

Guorong Wu is an Assistant Professor of Radiology and Biomedical Research Imaging Center (BRIC) in the University of North Carolina at Chapel Hill. Dr. Wu received his PhD degree from the Department of Computer Science in Shanghai Jiao Tong University in 2007. After graduation, he worked for Pixelworks and joined University of North Carolina at Chapel Hill in 2009. Dr. Wu's research aims to develop computational tools for biomedical imaging analysis and computer assisted diagnosis. He is interested in medical image processing, machine learning and pattern recognition.

The future of labour cannot involve protecting workers against disruption at the expense of new business models, Second Minister for Manpower and Home Affairs Josephine Teo said yesterday. Businesses and governments must work together to allay workers' concerns and make sure employees are able to take up the new jobs that will be created from automation and digitisation. "The catch is that the prospect of a net addition of jobs is comforting only to the extent that the workers involved can find ways to access the new opportunities," she said. "Otherwise, it is a frightening thought, and you could have a very unhappy situation where unemployment is rising and yet, at the same time, businesses are growing below potential." Mrs Teo, who is alsoMinister in the Prime Minister's Office, was speaking at the conclusion of the Milken Institute Asia Summit as part of a panel on preparing for jobs some two decades down the road.

Machine learning (ML) is fast becoming a litmus test for forward-thinking CIOs. Companies that fail to adopt machine learning for product development or business operations risk falling behind more nimble competitors in the coming decade. That's according to Dan Olley, who as the CTO of Elsevier, the scientific and health information unit of RELX Group, has ratcheted up his organization's adoption of ML technologies in recent years. "I fundamentally believe that we are at a tipping point with machine learning and it's going to change the way we interact with the digital world over the next decade," Olley told an audience of his peers last month at the CIO100 Symposium in Colorado Springs, Colo. "We're going to have decisions increasingly made by machines."