Ever since the first Terminator movie was released, we have seen portrayals of robots taking over the world. Now we are at the beginning of a process by which technology--specifically, artificial intelligence--will enable the disruption of the entertainment and media industries themselves. From traditional entertainment to gaming, this article explores how deepfake technology has become increasingly convincing and accessible to the public, and how much of an impact the harnessing of that technology will have on the entertainment and media ecosystem. What is a "Deepfake" and Why Does it Matter? The term "deepfake" refers to an AI-based technique that synthesizes media.

It's nearly time for Super Bowl Weekend, and that means it's a good time to consider an upgrade to your home theater set up. TVs and soundbars go on sale throughout the year, but the period right now and Black Friday are when you can expect to see some of the steepest discounts. So if you missed out a few months ago, now's your best chance until later this year. Check out our Best TVs and Best Soundbars guides for more of our recommendations. Special offer for Gear readers: Get a 1-year subscription to WIRED for $5 ($25 off).

This paper introduces a new efficient autoprecoder (AP) based deep learning approach for massive multiple-input multiple-output (mMIMO) downlink systems in which the base station is equipped with a large number of antennas with energy-efficient power amplifiers (PAs) and serves multiple user terminals. We present AP-mMIMO, a new method that jointly eliminates the multiuser interference and compensates the severe nonlinear (NL) PA distortions. Unlike previous works, AP-mMIMO has a low computational complexity, making it suitable for a global energy-efficient system. Specifically, we aim to design the PA-aware precoder and the receive decoder by leveraging the concept of autoprecoder, whereas the end-to-end massive multiuser (MU)-MIMO downlink is designed using a deep neural network (NN). Most importantly, the proposed AP-mMIMO is suited for the varying block fading channel scenario. To deal with such scenarios, we consider a two-stage precoding scheme: 1) a NN-precoder is used to address the PA non-linearities and 2) a linear precoder is used to suppress the multiuser interference. The NN-precoder and the receive decoder are trained off-line and when the channel varies, only the linear precoder changes on-line. This latter is designed by using the widely used zero-forcing precoding scheme or its lowcomplexity version based on matrix polynomials. Numerical simulations show that the proposed AP-mMIMO approach achieves competitive performance with a significantly lower complexity compared to existing literature. Index Terms-multiuser (MU) precoding, massive multipleinput multiple-output (MIMO), energy-efficiency, hardware impairment, power amplifier (PA) nonlinearities, autoprecoder, deep learning, neural network (NN)

The need to analyze information from streams arises in a variety of applications. One of the fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the existence of the pattern in transactions but pay no attention to the series of itemsets and their multiple occurrences. The pattern over a window of itemsets stream and their multiple occurrences, however, provides additional capability to recognize the essential characteristics of the patterns and the inter-relationships among them that are unidentifiable by the existing items and existence based studies. In this paper, we study such a new sequential pattern mining problem and propose a corresponding efficient sequential miner with novel strategies to prune search space efficiently. Experiments on both real and synthetic data show the utility of our approach.

Fox News Flash top entertainment and celebrity headlines are here. Check out what clicked this week in entertainment. Angie Harmon is reflecting on the new direction her career has taken since she ended her reign as Jane Clementine Rizzoli on "Rizzoli & Isles." The 49-year-old spoke to reporters on Wednesday about her latest role in the Lifetime original film, "Buried in Barstow," in which Harmon stars as Hazel King, a single mother who is "determined to shield her daughter from the life she once had while protecting and defending those who can't protect themselves." "Plucked off the streets of Las Vegas at 15, Hazel was trained as a hitwoman until a surprising pregnancy drives her to leave it all behind," the show's synopsis explains.

Machine learning models are usually developed in a training environment (online or offline) and then can be deployed to be used with live data. If you're working in Data Science and Machine learning projects, knowing how to deploy a model is one of the most important skills you'll need to have. Who is this article for? This article is for those who have created a machine learning model in a local machine and want to deploy and test the model within a short time. It's also for those who are looking for an alternative platform to deploy their machine learning models.

In this course, we aim to specialize in artificial intelligence by working on 14 Machine Learning Projects and Deep Learning Projects at various levels (easy - medium - hard). Before starting the course, you should have basic Python knowledge. Our aim in this course is to turn real-life problems that seem difficult to do into projects and then solve them using latest versions of artificial intelligence algorithms (machine learning algortihms and deep learning algorithms) and Python(3.8). This course was prepared in August 2021. We will carry out some of our projects using machine learning and some using deep learning algorithms.

Singing melody extraction is an important problem in the field of music information retrieval. Existing methods typically rely on frequency-domain representations to estimate the sung frequencies. However, this design does not lead to human-level performance in the perception of melody information for both tone (pitch-class) and octave. In this paper, we propose TONet, a plug-and-play model that improves both tone and octave perceptions by leveraging a novel input representation and a novel network architecture. First, we present an improved input representation, the Tone-CFP, that explicitly groups harmonics via a rearrangement of frequency-bins. Second, we introduce an encoder-decoder architecture that is designed to obtain a salience feature map, a tone feature map, and an octave feature map. Third, we propose a tone-octave fusion mechanism to improve the final salience feature map. Experiments are done to verify the capability of TONet with various baseline backbone models. Our results show that tone-octave fusion with Tone-CFP can significantly improve the singing voice extraction performance across various datasets -- with substantial gains in octave and tone accuracy.

The term"dox" is an abbreviation for"documents," and doxing is the act of disclosing private, sensitive, or personally identifiable information about a person without their consent. Sensitive information can be considered as any type of confidential information or any information that can be used to identify a person uniquely. This information is called doxed information and includes demographic information [53] such as birthday, sexual orientation, race, ethnicity, and religion, or location information which can be used to precisely or approximately locate a person such as the street address, ZIP code, IP address, and GPS coordinates. Other categories of doxed information are identity documents like passport number and social security number, contact information like phone number and email address, financial information such as credit card and bank account details, or sign-in credentials such as usernames and passwords[15]. Such disclosure may have various consequences. It may encourage forms of bigotry and hate groups, encourage human or child trafficking and endanger people's lives or reputations, scare and intimidate people by swatting

Young and dynamic data science and machine learning enthusiasts are all are very interested in making a career transition by learning and doing as much hands-on learning as possible with these technologies and concepts as Data Scientists or Machine Learning Engineers or Data Engineers or Data Analytics Engineers. I believe they must have the Project Experience and a job-winning portfolio in hand before they hit the interview process. Certainly, this interview process would be challenging, NOT only for the freshers, but also for experienced individuals since these are all new techniques, domain, process approach, and implementation methodologies that are totally different from traditional software development. Of course, we could adopt an agile mode of delivery and no excuse from modern cloud adoption techniques and state beyond all industries and domains, who are all looking and interested in artificial intelligence and machine learning (AI and ML) and its potential benefits. In this article, let's discuss how to choose the best data science and ML projects during the capstone stages of your schools, colleges, training institutions, and specific job-hunting perspective.