The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost due to increased data communication, (ii) threatened data privacy by allowing untrusted parties to utilise this information. Recently, in light of these limitations, a new emerging technique, coined as federated learning (FL), arose to bring ML to the edge of wireless networks. FL can extract the benefits of data silos by training a global model in a distributed manner, orchestrated by the FL server. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of FL. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth generation (B5G) and sixth generation (6G) communication systems. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

Participatory Budgeting (PB) is a process in which voters decide how to allocate a common budget; most commonly it is done by ordinary people -- in particular, residents of some municipality -- to decide on a fraction of the municipal budget. From a social choice perspective, existing research on PB focuses almost exclusively on designing computationally-efficient aggregation methods that satisfy certain axiomatic properties deemed "desirable" by the research community. Our work complements this line of research through a user study (N = 215) involving several experiments aimed at identifying what potential voters (i.e., non-experts) deem fair or desirable in simple PB settings. Our results show that some modern PB aggregation techniques greatly differ from users' expectations, while other, more standard approaches, provide more aligned results. We also identify a few possible discrepancies between what non-experts consider \say{desirable} and how they perceive the notion of "fairness" in the PB context. Taken jointly, our results can be used to help the research community identify appropriate PB aggregation methods to use in practice.

I recently wrote the book that I wish existed when I was introduced to machine learning: Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI. Most machine learning models are guided by human-annotated data, but most machine learning books and courses focus on algorithms. You can often get state-of-the-art results with good data and simple algorithms, but you rarely get state-of-the-art results from the best algorithm with bad data. So if you need to go deep in one area of machine learning first, you could argue that the data side is more important. In addition to the technical focus of the book, it features anecdotes from 11 machine learning experts. Each shared an anecdote about data-related problems they encountered building and evaluating machine learning models in real-world situations. Their stories tell us something important about machine learning leadership more broadly, with each anecdote tying into a lesson about running successful data science projects.

Huawei launched the IdeaHub Board Edu, a brand-new model from its Intelligent Collaboration product series. Announced during an online forum broadcast around the world, the new product is designed to support the digitalization of education and office. It features a range of upgraded functions including a smart whiteboard and wireless projection that ease the transition from off- to online collaboration. HUAWEI IdeaHub Board series plays an important role in facilitating digital education. It meets institutions' needs to create digital and collaborative classrooms, and offer hybrid learning.

The United States needs a better strategy and more advanced tools for information operations, Lt. Gen. Dennis Crall, the Joint Staff's chief information officer, said Thursday. The government has become slower and less confident in its approach, a reticence it can't afford as artificial intelligence drastically increases the pace of messaging and information campaigns, said Crall, who is also the Joit Staff's director for command, control, communications, computers, and cyber. . "The speed at which machines and AI won some of these information campaigns changes the game drastically for us. If we study, if we're hesitant, if we don't have good left and right lateral limits, if every operation requires a new set of permissions...We're never going to compete." Crall made his remarks at the NDIA conference for Special Operations and Low Intensity Conflict, or SOLIC.

Over the past decade, we have seen exponential growth in online content fueled by social media platforms. Data generation of this scale comes with the caveat of insurmountable offensive content in it. The complexity of identifying offensive content is exacerbated by the usage of multiple modalities (image, language, etc.), code mixed language and more. Moreover, even if we carefully sample and annotate offensive content, there will always exist significant class imbalance in offensive vs non offensive content. In this paper, we introduce a novel Code-Mixing Index (CMI) based focal loss which circumvents two challenges (1) code mixing in languages (2) class imbalance problem for Dravidian language offense detection. We also replace the conventional dot product-based classifier with the cosine-based classifier which results in a boost in performance. Further, we use multilingual models that help transfer characteristics learnt across languages to work effectively with low resourced languages. It is also important to note that our model handles instances of mixed script (say usage of Latin and Dravidian - Tamil script) as well. Our model can handle offensive language detection in a low-resource, class imbalanced, multilingual and code mixed setting.

"I'm increasingly coming to terms with the fact that I may never get married," said Mustafa, a 34-year-old Black Muslim man who asked that we not use his real name. He has been on two dates with women he met on dating apps in the past year – and they left him feeling fatigued and doubtful that he would ever find a genuine connection with someone. He had turned to the apps, he said, because, there is no dating scene in his British-Somali community. But, he lamented, "it's really hard to find someone. This is not how Mustafa imagined his life would be in his mid-thirties. When he was younger, he pictured himself as a devoted husband and loving father to a couple of children by now. In this mental image of familial bliss, he was also living in a picturesque cottage in the English countryside complete with "a lake or something". Instead, he recently celebrated his 34th birthday single and living in a flat overlooking the Wembley Stadium arch in North West London. But, he added with a shrug, "I've started learning how to cycle." Discussing his hobbies and interests – cycling, reading, writing – he sounds more optimistic. He has directed his energy away from the fickle and unpredictable pursuit of love and towards those variables of his life he can control, like picking up new pastimes. 'All they see is a Black guy' Although the United Kingdom's Black Muslim community is culturally diverse, including people from a wide range of African and Caribbean backgrounds, it only comprises 10 percent of the UK's Muslim population. This can make dating or finding a marriage partner particularly difficult. A recent survey by Muzmatch, a Muslim-specific dating app that has been heralded for helping 20,000 Muslims meet and marry since its launch in 2015, revealed the challenges faced by Black Muslims dating in the UK. Muzmatch asked 471 of their members from different ethnic groups if they felt that race and ethnicity affected the matches they received and whether they had negative experiences as a result of this. In their answers, Black users pointed to a range of issues – including fetishisation, colourism and discrimination. Most of the Black women surveyed complained about being fetishised and branded "exotic". One West African woman described how dark-skinned women were considered unattractive and how she had been called the n-word by one user. A Sudanese man expressed concern that he was matched with women with similar interests to him who subsequently rejected him because their family wouldn't accept him. "It doesn't matter if you're on your deen and have a successful career.

The global AI in fintech market size is projected to increase to $46,881.9 million by 2030 from $7,702.7 million in 2020, at a 19.8% CAGR between 2020 and 2030. With AI, the efficiency of financial processes and the security of money-related data can be improved massively. For instance, in regard to fraud detection, AI monitors people's online transactional behavior so that any deviation and a potential fraud can be identified in real time and stopped right there. Moreover, AI helps in automating several processes in the banking, financial services, and insurance (BFSI) sector, such as online customer engagement via chatbots, claims processing, and answering frequently asked questions (FAQs). This not only allows BFSI companies to reduce their expenditure in hiring humans for these tasks but also engage these employees in more-important tasks, such as decision making and strategizing. AI solutions have been in a higher demand than managed and professional services because the former conduct question and answer (Q&A) processing, natural language processing (NLP) and generation, facial recognition, video and image analysis, and speech recognition.

A piece of computer history and coveted collector's item with ties to Southern California fetched six figures at auction this week. An Apple-1 computer, hand-built by Steve Wozniak and Steve Jobs in the 1970s, sold for $500,000 at auction Tuesday in Monrovia. The final bid for the unit was $400,000, with the buyer -- who wishes to remain anonymous -- paying an additional $100,000 premium, or commission, to John Moran Auctioneers. The Southern California-based auction house estimated that the unit, dubbed the "Chaffey College Apple-1" after its original owner was identified as a Chaffey professor, would sell for between $400,000 to $600,000. In 2014, Bonhams auction house sold an Apple-1 for more than $900,000.

In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task depending on many external factors. The topology of the scene and the interactions between the pedestrians are just some of them. Due to advancements in data-science and data collection technologies deep learning methods have recently become a research hotspot in numerous domains. Therefore, it is not surprising that more and more researchers apply these methods to predict trajectories of pedestrians. This paper compares these relatively new deep learning algorithms with classical knowledge-based models that are widely used to simulate pedestrian dynamics. It provides a comprehensive literature review of both approaches, explores technical and application oriented differences, and addresses open questions as well as future development directions. Our investigations point out that the pertinence of knowledge-based models to predict local trajectories is nowadays questionable because of the high accuracy of the deep learning algorithms. Nevertheless, the ability of deep-learning algorithms for large-scale simulation and the description of collective dynamics remains to be demonstrated. Furthermore, the comparison shows that the combination of both approaches (the hybrid approach) seems to be promising to overcome disadvantages like the missing explainability of the deep learning approach.