Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, privacy is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations, read books, bought items) with a central server. Unfortunately, data harvesting and collection is at the basis of modern, state-of-the-art approaches to recommendation. Decreased users' willingness to share personal information along with data minimization/protection policies (such as the European GDPR), can result in the "data scarcity" dilemma affecting data-intensive applications such as recommender systems (RS). We argue that scarcity of adequate data due to privacy concerns can severely impair the quality of learned models and, in the long term, result in a turnover and disloyal customers with direct consequences for lives, society, and businesses. To address these issues, we present FPL, an architecture in which users collaborate in training a central factorization model while controlling the amount of sensitive data leaving their devices. The proposed approach implements pair-wise learning to rank optimization by following the Federated Learning principles conceived originally to mitigate the privacy risks of traditional machine learning. We have conducted an extensive experimental evaluation on three Foursquare datasets and have verified the effectiveness of the proposed architecture concerning accuracy and beyond-accuracy objectives. We have analyzed the impact of communication cost with the central server on the system's performance, by varying the amount of local computation and training parallelism. Finally, we have carefully examined the impact of disclosed users' information on the quality of the final model and ...

The market is further segmented on the basis of types and end-user applications. The report also provides an estimation of the segment expected to lead the market in the forecast years. Detailed segmentation of the market based on types and applications along with historical data and forecast estimation is offered in the report. Furthermore, the report provides an extensive analysis of the regional segmentation of the market. The regional analysis covers product development, sales, consumption trends, regional market share, and size in each region.

The market is further segmented on the basis of types and end-user applications. The report also provides an estimation of the segment expected to lead the market in the forecast years. Detailed segmentation of the market based on types and applications along with historical data and forecast estimation is offered in the report. Furthermore, the report provides an extensive analysis of the regional segmentation of the market. The regional analysis covers product development, sales, consumption trends, regional market share, and size in each region.

Analysis on Strategies of Leading Players: Market players can use this analysis to gain competitive advantage over their competitors in the Artificial Intelligence for Accounting market. Study on Key Market Trends: This section of the report offers a deeper analysis of the latest and future trends of the Artificial Intelligence for Accounting market. Market Forecasts: Buyers of the report will have access to accurate and validated estimates of the total market size in terms of value and volume. The report also provides consumption, production, sales, and other forecasts for the Artificial Intelligence for Accounting market. Regional Growth Analysis: All major regions and countries have been covered in the report.

Facial recognition technology is now common in a growing number of places around the world from public CCTV cameras to biometric identification systems in airports already touching half of the global population on a regular basis. Visualizations from SurfShark classify 194 countries and regions based on the extent of surveillance. More recently, the Department of Homeland Security unveiled its "Biometric Exit" plan, which aims to use facial recognition technology on nearly all air travel passengers by 2023, to identify compliance with visa status. Perhaps surprisingly, 59% of Americans are actually in favour of implementing facial recognition technology, considering it acceptable for use in law enforcement according to a Pew Research survey. Yet, some cities such as San Francisco have pushed to ban surveillance, citing a stand against its potential abuse by the government. Facial recognition technology can potentially come in handy after a natural disaster.

This market research report offers a comprehensive analysis of the event management service market's growth based on end-users and geography. The study report offers a comprehensive analysis of Artificial Intelligence (AI) in BFSI market size across the globe as regional and country level market size analysis, CAGR estimation of industry growth during the forecast period, revenue, key drivers, competitive background and sales analysis of the payers. Along with that, the report explains the major challenges and risks to face in the forecast period. The research report on the Global Artificial Intelligence (AI) in BFSI market helps clients to understand the structure of the market by identifying its various segments such as product type, end user, competitive landscape and key regions. Further, the report helps users to analyze trends in each sub segment of the Global Artificial Intelligence (AI) in BFSI industry.

We're only beginning to understand the full potential of AI, but here's what we do know: without doubt, it's foundational technology for IoT and the fourth industrial revolution. We also know that comp sci is just scratching the surface of what's possible well within the decade, with outcomes that not long ago would have caused more hilarity than knowing nods. "The fervor around state-of-the-art AI language models like Open AI's GPT-3 hasn't died down. Melanie Mitchell, a professor of computer science at Portland State University, found evidence that GPT-3 can make primitive analogies. Raphaël Millière, a philosopher of mind and cognitive science at Columbia University's Center for Science and Society, asked GPT-3 to compose a response to the philosophical essays written about it. Among other applications, the API providing access to the model has been used to create a recipe generator, an all-purpose Excel function, and a comedy sketch writer."

Generative adversarial networks(GANs) took the Machine Learning field by storm last year with those impressive fake human-like faces. Bonus Point* They are basically generated from nothing. Irrefutably, GANs implements implicit learning methods where the model learns without the data directly passing through the network, unlike those explicit techniques where weights are learned directly from the data. Okay, suppose in the city of Rio de Janeiro, money forging felonies are increasing so a department is appointed to check in these cases. Detectives are expected to classify the legit ones and fake ones.

Robots are becoming an important way to deliver health care, and personality is vital to understanding their effectiveness. Despite this, there is a lack of a systematic overarching understanding of personality in health care human robot interaction (H-HRI). To address this, the authors conducted a review that identified 18 studies on personality in H-HRI. This paper presents the results of that systematic literature review. Insights are derived from this review regarding the methodologies, outcomes, and samples utilized. The authors of this review discuss findings across this literature while identifying several gaps worthy of attention. Overall, this paper is an important starting point in understanding personality in H-HRI.

Technovation, a global technology education nonprofit, announced that two teams of girls and two family teams - representing Kazakhstan, Kuwait, India and Ireland - were named winners at its annual Technovation World Summit held virtually August 13-14. The two-day event brought together more than 1,000 members and supporters of the Technovation community from around the world. The Awards Ceremony, held during World Summit, is a culmination of the annual Technovation Girls and Technovation Families programs in which nearly 2,000 teams of girls (ages 10-18) and families (with children ages 8-16) are challenged to develop a mobile application or AI prototype to solve an issue they've identified in their community. This year, teams across 60 countries overcame incredible odds stemming from COVID-19 to participate with the support of more than 3,500 mentors and chapter ambassadors. All finalists will receive a portion of the nearly $30,000 being awarded.