--Social media has become integral to minors' daily lives and is used for various purposes, such as making friends, exploring shared interests, and engaging in educational activities. However, the increase in screen time has also led to heightened challenges, including cyberbullying, online grooming, and exploitations posed by malicious actors. Traditional content moderation techniques have proven ineffective against exploiters' evolving tactics. T o address these growing challenges, we propose the EdgeAIGuard content moderation approach that is designed to protect minors from online grooming and various forms of digital exploitation. The proposed method comprises a multi-agent architecture deployed strategically at the network edge to enable rapid detection with low latency and prevent harmful content targeting minors. The experimental results show the proposed method is significantly more effective than the existing approaches. Social media platforms have fundamentally transformed how individuals communicate, connect, and share information. It is not an exaggeration to say that social media has become integral to our daily lives. For minors, these platforms serve to form their identities, express themselves, and interact socially [1]. A recent study revealed that approximately 84% of teenagers aged 13 to 17 actively use social media for an average of 4.8 hours daily [2]. Platforms like Snapchat, TikTok, and Instagram are easily accessible on devices like smartphones and wearables, allowing users to share their personal experiences while engaging with diverse content.

Computer-mediated communication has become more important than face-to-face communication in many contexts. Tracking emotional dynamics in chat conversations can enhance communication, improve services, and support well-being in various contexts. This paper explores a hybrid approach to tracking emotional dynamics in chat conversations by combining DistilBERT-based text emotion detection and emoji sentiment analysis. A Twitter dataset was analyzed using various machine learning algorithms, including SVM, Random Forest, and AdaBoost. We contrasted their performance with DistilBERT. Results reveal DistilBERT's superior performance in emotion recognition. Our approach accounts for emotive expressions conveyed through emojis to better understand participants' emotions during chats. We demonstrate how this approach can effectively capture and analyze emotional shifts in real-time conversations. Our findings show that integrating text and emoji analysis is an effective way of tracking chat emotion, with possible applications in customer service, work chats, and social media interactions.

In the contemporary digital era, the Internet functions as an unparalleled catalyst, dismantling geographical and linguistic barriers particularly evident in texting. This evolution facilitates global communication, transcending physical distances and fostering dynamic cultural exchange. A notable trend is the widespread use of transliteration, where the English alphabet is employed to convey messages in native languages, posing a unique challenge for language technology in accurately detecting the source language. This paper addresses this challenge through a dataset of phone text messages in Hindi and Russian transliterated into English utilizing BERT for language classification and Google Translate API for transliteration conversion. The research pioneers innovative approaches to identify and convert transliterated text, navigating challenges in the diverse linguistic landscape of digital communication. Emphasizing the pivotal role of comprehensive datasets for training Large Language Models LLMs like BERT, our model showcases exceptional proficiency in accurately identifying and classifying languages from transliterated text. With a validation accuracy of 99% our models robust performance underscores its reliability. The comprehensive exploration of transliteration dynamics supported by innovative approaches and cutting edge technologies like BERT, positions our research at the forefront of addressing unique challenges in the linguistic landscape of digital communication. Beyond contributing to language identification and transliteration capabilities this work holds promise for applications in content moderation, analytics and fostering a globally connected community engaged in meaningful dialogue.

Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware features directly from the source signal, and the discrete-time analog transmission (DTAT) of these features. Compared with traditional digital communications, semantic communications with DeepJSCC provide superior reconstruction performance at the receiver and graceful degradation with diminishing channel quality, but also exhibit a large peak-to-average power ratio (PAPR) in the transmitted signal. An open question has been whether the gains of DeepJSCC come from the additional freedom brought by the high-PAPR continuous-amplitude signal. In this paper, we address this question by exploring three PAPR reduction techniques in the application of image transmission. We confirm that the superior image reconstruction performance of DeepJSCC-based semantic communications can be retained while the transmitted PAPR is suppressed to an acceptable level. This observation is an important step towards the implementation of DeepJSCC in practical semantic communication systems.

SafeGuard, a cloud platform designed to protect assets from cybersecurity threats and risk factors, today announced it has raised $45 million in a mix of equity and debt. SafeGuard, which was founded in 2014, develops products that identify risks in communication channels such as social media, chat apps, and collaboration platforms -- like Slack, LinkedIn, and WhatsApp. SafeGuard also helps companies take action and claims it can shield high-profile or targeted individuals from account takeovers, spearphishing, malicious content, threats of violence, and misinformation, as well as bad actor connections. To this end, SafeGuard leverages an AI-powered engine called Threat Cortex that detects and spotlights risks across different attack surfaces. On the compliance side of the equation, SafeGuard offers a tool that taps AI to alert employees, customers, and partners if their digital communications are at risk of violating regulations like the Financial Industry Regulatory Authority and Financial Conduct Authority.

We have enough problems these days. The last thing we need to worry about is our health or the high costs of care. That's why many people are turning to digital health solutions. As noted by The Wall Street Journal, many of these solutions can reportedly help manage diabetes, improve sleep, monitor heart health, encourage weight loss, track whether patients are sticking to physical therapy regimens, and more.(3) Due to the recent health scare, governments and healthcare systems worldwide may now realize how essential digital health has become.

After all, mined minerals have become essential for your cell phones, electric vehicles, solar panels, wind turbines, your computers, you name it. Plus, with a growing population, urbanization, demand for green energy, buildings, cars, and even more electronic gadgets, we could very well see an increased need for metals. What could make mining even more valuable, though, is that we already seem to be coming up short on essential metals, like copper, silver, platinum, palladium, nickel, cobalt, and rhodium. Goldman Sachs, for one, appeared to warn of a looming shortage of copper.(2) Also, "Fitch Ratings has revised some of its metals and mining price assumptions as prices for many commodities will benefit in the short term from returning demand while the supply response remains slow and inventories are running low," the company said in a report.(3)

Enterprises, in particular, are overwhelmed with communications from inside and outside of their organizations. Corporate email continues to be at the heart of this flood of data, with content that needs to be acted on in a timely manner. Consider these facts: – In 2019, more than 128.8 billion business emails were sent and received per day. This impairs productivity in matters where speed is critical, resulting in operational delays and rising costs. The product accomplishes this by understanding the semantic content--the meaning and intent of the messages – at massive scale in real time.

Our ability to communicate effectively is something that makes us uniquely human. In fact, it has been instrumental in cementing the progress we have made throughout history as a species. The spoken word made it possible for humans to work together and was the first method we developed for preserving information. Stories of historical events, myths, legends and folklore - all survived the passage of time, for centuries, through word of mouth alone. The written language came next, further amplifying how humans communicate.

Artificial intelligence programmers are developing bots that can identify digital bullying and sexual harassment. Known as "#MeTooBots" after the high-profile movement that arose after allegations against the Hollywood producer Harvey Weinstein, the bots can monitor and flag communications between colleagues and are being introduced by companies around the world. Bot-makers say it is not easy to teach computers what harassment looks like, with its linguistic subtleties and grey lines. Jay Leib, the chief executive of the Chicago-based AI firm NexLP, said: "I wasn't aware of all the forms of harassment. I thought it was just talking dirty. It comes in so many different ways. It might be 15 messages … it could be racy photos."