Someday, an artificial intelligence will write these lines for us; The advancement of these technologies is terrifying for one simple reason: the things they've been able to build on their own. But since our time has not yet come, all we can do is enjoy these kinds of inventions. Have you ever dreamed of a Harry Potter animated series? Well, AI has imagined what it would be like, and the truth is that its appearance is spectacular. Below we show you what it looks like, and here you can see some more pictures.

It has all of the features and capabilities of a modern programming language, which can be a major advantage when building complex machine-learning …

If you're trying to build a brand online, picking up followers on social media is vital. To stand out from the crowd, you need to post content that really catches the eye. The Air NEO Selfie Camera Drone makes that task much easier. This smart device can auto-fly from the palm of your hand to take a variety of images from an aerial perspective. It's normally priced at $159.95, but you can grab it for just $129.99 in an extended Cyber Monday deal at TechRepublic Academy.

How does Twitter use artificial intelligence and machine learning? Twitter uses large-scale machine learning and AI for sentiment analysis, bot analysis and detection of fake accounts, image classification and more. From Amazon to Instagram, Sephora, Microsoft, and Twitter, AI will shape the future of speech in America and beyond. The big question is not if they use it, but how it is being used, and what impact will this have on consumer privacy in the future. For the past fifteen years, I have been a national commentator on the politics of big tech and social media platforms. Social Media content decisions have become highly political, and artificial intelligence has proliferated this process at scale. But somewhere along the way, the public was left in the dark on just how large of a role machine learning plays in large-scale content operations in Silicon Valley. While the national conversation on free speech focuses on high-profile executives of tech companies and how content ...

Hi! My name is Abdul Rafay, and I work as a Web Development and Machine Learning Engineer. I recently finished my studies, and I've been working …

The high feature dimensionality is a challenge in music emotion recognition. There is no common consensus on a relation between audio features and emotion. The MER system uses all available features to recognize emotion; however, this is not an optimal solution since it contains irrelevant data acting as noise. In this paper, we introduce a feature selection approach to eliminate redundant features for MER. We created a Selected Feature Set (SFS) based on the feature selection algorithm (FSA) and benchmarked it by training with two models, Support Vector Regression (SVR) and Random Forest (RF) and comparing them against with using the Complete Feature Set (CFS). The result indicates that the performance of MER has improved for both Random Forest (RF) and Support Vector Regression (SVR) models by using SFS. We found using FSA can improve performance in all scenarios, and it has potential benefits for model efficiency and stability for MER task. NTRODUCTION Music has become an indispensable part of people's lives. It plays a vital role in our world. We use music in almost every field, such as public places, entertainment, and even therapy. As the technology grows, the widespread adoption of digital audio formats, especially MP3, music distribution has become very efficient and seamless. The primary method of music consumption has shifted from retail stores to online and internet-based distribution channels. Subscription services had now become popular where the consumers now have access to much larger libraries than when albums were purchased individually. Traditional approaches to managing digital music libraries using of embedded metadata are no longer sufficient to deal with such a large database since the text cannot fully convey the expression of the musical content [1] [2], therefore the content-based music retrieval system can be ideal to handle this task more efficiency and opens a new perspective to discover music.

Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named "pyleetspeak" to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective.

Due to the severity of the social media offensive and hateful comments in Brazil, and the lack of research in Portuguese, this paper provides the first large-scale expert annotated corpus of Brazilian Instagram comments for hate speech and offensive language detection. The HateBR corpus was collected from the comment section of Brazilian politicians' accounts on Instagram and manually annotated by specialists, reaching a high inter-annotator agreement. The corpus consists of 7,000 documents annotated according to three different layers: a binary classification (offensive versus non-offensive comments), offensiveness-level classification (highly, moderately, and slightly offensive), and nine hate speech groups (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology for the dictatorship, antisemitism, and fatphobia). We also implemented baseline experiments for offensive language and hate speech detection and compared them with a literature baseline. Results show that the baseline experiments on our corpus outperform the current state-of-the-art for the Portuguese language.

Spectrum coexistence is essential for next generation (NextG) systems to share the spectrum with incumbent (primary) users and meet the growing demand for bandwidth. One example is the 3.5 GHz Citizens Broadband Radio Service (CBRS) band, where the 5G and beyond communication systems need to sense the spectrum and then access the channel in an opportunistic manner when the incumbent user (e.g., radar) is not transmitting. To that end, a high-fidelity classifier based on a deep neural network is needed for low misdetection (to protect incumbent users) and low false alarm (to achieve high throughput for NextG). In a dynamic wireless environment, the classifier can only be used for a limited period of time, i.e., coherence time. A portion of this period is used for learning to collect sensing results and train a classifier, and the rest is used for transmissions. In spectrum sharing systems, there is a well-known tradeoff between the sensing time and the transmission time. While increasing the sensing time can increase the spectrum sensing accuracy, there is less time left for data transmissions. In this paper, we present a generative adversarial network (GAN) approach to generate synthetic sensing results to augment the training data for the deep learning classifier so that the sensing time can be reduced (and thus the transmission time can be increased) while keeping high accuracy of the classifier. We consider both additive white Gaussian noise (AWGN) and Rayleigh channels, and show that this GAN-based approach can significantly improve both the protection of the high-priority user and the throughput of the NextG user (more in Rayleigh channels than AWGN channels).

Congratulations, you made it through another rough year. We've traditionally reserved the end of the year as an opportunity to have a little fun while shining a light on the year's biggest failings in tech, with a roundup affectionately known as CNET's annual Tech Turkeys. In the past, the ribbing was good natured, pointing out silly products or a random faux pas at a conference (hello, Michael Bay!). Then the problems across Big Tech piled on. Congressional hearings, election-swinging misinformation and privacy-invading breaches became a regular thing.