This paper addresses the task of score following in sheet music given as unprocessed images. While existing work either relies on OMR software to obtain a computer-readable score representation, or crucially relies on prepared sheet image excerpts, we propose the first system that directly performs score following in full-page, completely unprocessed sheet images. Based on incoming audio and a given image of the score, our system directly predicts the most likely position within the page that matches the audio, outperforming current state-of-the-art image-based score followers in terms of alignment precision. We also compare our method to an OMR-based approach and empirically show that it can be a viable alternative to such a system.

Over three decades, the Internet has grown from a small network of computers used by research scientists to communicate and exchange data to a technology that has penetrated almost every aspect of our day-to-day lives. Today, it is hard to imagine a life without online access for doing business, shopping, and socialising. A technology that has connected humanity at a scale never before possible has also amplified some of our worst qualities. Online hate speech spreads virally across the globe with short and long term consequences for individuals and societies. These consequences are often difficult to measure and predict. Online social media websites and mobile apps have inadvertently become the platform for the spread and proliferation of hate speech. "Hate speech is a type of speech that takes place online (e.g., the Internet, online social media platforms) with the purpose to attack a person or a group on the basis of attributes such as race, religion, ethnic origin, sexual orientation, disability, or gender."

Purpose: This paper presents an algorithm that can elicitate (infer) all or any combination of ELECTRE Tri-B parameters. For example, a decision-maker can maintain the values for indifference, preference, and veto thresholds, and our algorithm can find the criteria weights, reference profiles, and the lambda cutting level. Our approach is inspired by a Machine Learning ensemble technique, the Random Forest, and for that, we named our approach as ELECTRE Tree algorithm. Methodology: First, we generate a set of ELECTRE Tri-B models, where each model solves a random sample of criteria and alternatives. Each sample is made with replacement, having at least two criteria and between 10% to 25% of alternatives. Each model has its parameters optimized by a genetic algorithm that can use an ordered cluster or an assignment example as a reference to the optimization. Finally, after the optimization phase, two procedures can be performed, the first one will merge all models, finding in this way the elicitated parameters, and in the second procedure each alternative is classified (voted) by each separated model, and the majority vote decides the final class. Findings: We have noted that concerning the voting procedure, non-linear decision boundaries are generated, and they can be suitable in analyzing problems with the same nature. In contrast, the merged model generates linear decision boundaries. Originality: The elicitation of ELECTRE Tri-B parameters is made by an ensemble technique that is composed of a set of multicriteria models that are engaged in generating robust solutions.

It stores all scraped samples locally and runs rules over this data to either aggregate and record new time series from existing data or generate alerts. Grafana or other API consumers can be used to visualize the collected data.

Human fascination with robots has long been fused with fear. The first widespread use of the term came a century ago in a Czech play about robots manufactured to serve and work for people. The bots turn on their masters. That plot has played out in fiction countless times since. Meanwhile, the real world has created ever more advanced versions of mechanical servants.

The rising demand for Deep Learning is so massive and complex that we are reaching the computational limits of the technology. A recent study suggests that progress in deep learning is heavily dependent on the increase in computational abilities. Researchers from Massachusetts Institute of Technology (MIT), MIT-IBM Watson AI Lab, Underwood International College, and the University of Brasilia found in a recent study that deep learning is strong reliant on the increase in compute. The researchers believe that the continuous progress in Deep Learning will require dramatically more computational methods. In the research paper, co-authors wrote, "We show deep learning is not computationally expensive by accident, but by design. The same flexibility that makes it excellent at modelling diverse phenomena and outperforming expert models also makes it dramatically more computationally expensive. Despite this, we find that the actual computational burden of deep learning models is scaling more rapidly than (known) lower bounds from theory, suggesting that substantial improvements might be possible."

The study states that deep learning's impressive progress has come with a "voracious appetite for computing power." Researchers at the Massachusetts Institute of Technology, MIT-IBM Watson AI Lab, Underwood International College, and the University of Brasilia have found that we are reaching computational limits for deep learning. The new study states that deep learning's progress has come with a "voracious appetite for computing power" and that continued development will require "dramatically" more computationally efficient methods. "We show deep learning is not computationally expensive by accident, but by design. The same flexibility that makes it excellent at modeling diverse phenomena and outperforming expert models also makes it dramatically more computationally expensive," the coauthors wrote.

Human fascination with robots has long been fused with fear. The first widespread use of the term came a century ago in a Czech play about robots manufactured to serve and work for people. The bots turn on their masters. That plot has played out in fiction countless times since. Meanwhile, the real world has created ever more advanced versions of mechanical servants.

- It’s not just the U.S., China and Russia who are embedding artificial intelligence into their military systems. - The U.K., Israel, Brazil, Australia, South Korea and Iran are also investing in military AI. - China’s private-public co-operative model is allowing it to take the lead from the U.S. in some key technologies. Think of artificial intelligence, and the mind often goes to industrial robots and benign surveillance systems. Increasingly, though, these are steppingstones for Big Brother to enhance capabilities in domestic security and international military warfare. China has co-opt...

Think of artificial intelligence, and the mind often goes to industrial robots and benign surveillance systems. Increasingly, though, these are steppingstones for Big Brother to enhance capabilities in domestic security and international military warfare. China has co-opted a controversial big data policing program into law enforcement, both for racial profiling of its Uighur minority population and for broader citizen surveillance through facial recognition. Wuhan has an entirely AI-staffed police station. But experts say China's artificial intelligence research is also being adapted for unconventional military warfare in the country's bid to dominate the field over the next decade.