"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.

Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications. However, existing methods mainly focus on improving the performance and often ignore the confidence of prediction for diverse situations. In this study, we are devoted to trustworthy multimodal regression which is critical in cost-sensitive domains. To this end, we introduce a novel Mixture of Normal-Inverse Gamma distributions (MoNIG) algorithm, which efficiently estimates uncertainty in principle for adaptive integration of different modalities and produces a trustworthy regression result. Our model can be dynamically aware of uncertainty for each modality, and also robust for corrupted modalities. Furthermore, the proposed MoNIG ensures explicitly representation of (modality-specific/global) epistemic and aleatoric uncertainties, respectively. Experimental results on both synthetic and different real-world data demonstrate the effectiveness and trustworthiness of our method on various multimodal regression tasks (e.g., temperature prediction for superconductivity, relative location prediction for CT slices, and multimodal sentiment analysis).

One of the main challenges for hierarchical clustering is how to appropriately identify the representative points in the lower level of the cluster tree, which are going to be utilized as the roots in the higher level of the cluster tree for further aggregation. However, conventional hierarchical clustering approaches have adopted some simple tricks to select the "representative" points which might not be as representative as enough. Thus, the constructed cluster tree is less attractive in terms of its poor robustness and weak reliability. Aiming at this issue, we propose a novel hierarchical clustering algorithm, in which, while building the clustering dendrogram, we can effectively detect the representative point based on scoring the reciprocal nearest data points in each sub-minimum-spanning-tree. Extensive experiments on UCI datasets show that the proposed algorithm is more accurate than other benchmarks. Meanwhile, under our analysis, the proposed algorithm has O(nlogn) time-complexity and O(logn) space-complexity, indicating that it has the scalability in handling massive data with less time and storage consumptions.

Current authentication and trusted systems depend on classical and biometric methods to recognize or authorize users. Such methods include audio speech recognitions, eye, and finger signatures. Recent tools utilize deep learning and transformers to achieve better results. In this paper, we develop a deep learning constructed model for Arabic speakers identification by using Wav2Vec2.0 and HuBERT audio representation learning tools. The end-to-end Wav2Vec2.0 paradigm acquires contextualized speech representations learnings by randomly masking a set of feature vectors, and then applies a transformer neural network. We employ an MLP classifier that is able to differentiate between invariant labeled classes. We show several experimental results that safeguard the high accuracy of the proposed model. The experiments ensure that an arbitrary wave signal for a certain speaker can be identified with 98% and 97.1% accuracies in the cases of Wav2Vec2.0 and HuBERT, respectively.

This is the accepted version of an article with the same name, published in the Special Issue "Federated Learning and Blockchain Supported Smart Networking in Beyond 5G (B5G) Wireless Communication" in Computer Networks. Abstract Federated machine learning (FL) allows to collectively train models on sensitive data as only the clients' models and not their training data need to be shared. However, despite the attention that research on FL has drawn, the concept still lacks broad adoption in practice. One of the key reasons is the great challenge to implement FL systems that simultaneously achieve fairness, integrity, and privacy preservation for all participating clients. To contribute to solving this issue, our paper suggests a FL system that incorporates blockchain technology, local differential privacy, and zero-knowledge proofs. Our implementation of a proof-of-concept with multiple linear regression illustrates that these state-of-the-art technologies can be combined to a FL system that aligns economic incentives, trust, and confidentiality requirements in a scalable and transparent system. A Blockchain blockchain eliminates the need for a centralized authority, provides transparency, enforces the federated learning protocol, and provides a decentralized infrastructure for the collection of fees and the distribution of rewards. The reward payment is calculated based on the client's clients' Federated learning enables multiple clients FIM Research Center 1. Introduction The application of machine learning (ML) promises far-reaching potentials across industries [1]. ML has already proven successful in many areas, such as web search or recommender systems in e-commerce, in which a lot of high-quality data exists [2]. While researchers address ML's growing demand for compute power and use of data with, e.g., distributed ML approaches where multiple computing nodes share their resources [3, 4, 5] and quality issues with data processing, access to data is not only a technical issue. Both traditional ML and distributed ML approaches assume that their training data is centralized by nature, preventing the applicability of ML approaches to domains in which data is sensitive and distributed at the same time. To avoid that ML approaches must rely on data to which only a centralized organization or individual has full access, federated machine learning (FL) can aggregate the less sensitive ML models that were independently and locally trained by individual clients [6, 7].

Artificial intelligence is quickly changing the way fintech, insurtech and 5G operate during the covid-19 crisis and beyond. Machine learning and artificial intelligence are improving Fintech by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools. Since that time the Covid-19 crisis and tragedy arose. On the one hand Paul Clarke noted that UK fintech investment slumps by 40% amid Covid-19 crisis, whilst on the other Deloitte in Beyond COVID-19: New opportunities for Fintech companies note that "As the COVID-19 pandemic continues to create uncertainty, many fintechs are under stress on a number of fronts. But, as the broader economy shifts from "respond" to "recover", new opportunities may be created for some fintechs. A key question is how fintechs may leverage their unique assets and skills to seize new opportunities in the future. It could be an opportune time to think big and act boldly." Pavitra R considered the impact of Covid-19 and noted in 5 U.S. FinTech startups reimagining the healthcare industry notes that FinTech is undoubtedly shaping the face of the Health Care industry. "FinTech companies leverage powerful innovations blockchain, Artificial Intelligence, and Machine Learning to eliminate the inefficiencies and knowledge gaps endemic to most healthcare payment plans." The likes of Nigel Wilson (@nigewillson) and Brian Ahier (@ahier) have stressed the importance of applying AI to positive use cases such as preventative medicine and improved Health Care outcomes. McKinsey in an article entitled AI-bank of the future: Can banks meet the AI challenge? " The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually (Exhibit 1)." Source for image above: AI-bank of the future: Can banks meet the AI challenge? "While for many financial services firms, the use of AI is episodic and focused on specific use cases, an increasing number of banking leaders are taking a comprehensive approach to deploying advanced AI, and embedding it across the full lifecycle, from the front- to the back-office (Exhibit 2)" Source for image above: AI-bank of the future: Can banks meet the AI challenge?

New Jersey, United States,- A recent market research report added to the repository of Verified Market Reports is an in-depth analysis of the Machine Learning Artificial intelligence Market. On the basis of historic growth analysis and the current scenario of the Machine Learning Artificial intelligence marketplace, the report intends to offer actionable insights on Global market growth projections. Authenticated data presented in the report is based on findings of extensive primary and secondary research. Insights drawn from data serve as excellent tools that facilitate a deeper understanding of multiple aspects of the Machine Learning Artificial intelligence market. This further helps users with their developmental strategy.