Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25, 256 MRI scans from 6, 314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing.

Abstract--Artificial Intelligence (AI) has found its applications in a variety of environments ranging from data science to cybersecurity. AI helps break through the limitations of traditional algorithms and provides more efficient and flexible methods for solving problems. In this paper, we focus on the applications of artificial intelligence in authentication, which is used in a wide range of scenarios including facial recognition to access buildings, keystroke dynamics to unlock smartphones. With the emerging AI-assisted authentication schemes, our comprehensive survey provides an overall understanding on a high level, which paves the way for future research in this area. In contrast to other relevant surveys, our research is the first of its kind to focus on the roles of AI in authentication. Learning and neural networks are The traditional password-based authentication method has two main mechanisms used in AI. Learning is the process of slowly faded out due to its inadequate ...

Vumacam, an international technology company, is now building out more applications on Proof 360 for the South African market, including a system to detect license plate cloning – when two cars show up in different locations with identical plate numbers. It's also opening up the platform for third-party developers to add their own applications and distribute them to its users. Later this year, Ricky Croock Chief Executive Officer at Vumacam Johannesburg Metropolitan Area118, says that the company will switch to a new model, where customers will pay a flat fee to get access to the full network of cameras instead of just a selection. Agencies will still be able to filter the alerts to their jurisdiction, but they will also be able to view any feed in the country. The new approach will allow Vumacam to place poles and cameras irrespective of whether there are paying customers nearby.

As part of efforts towards making medications affordable and accessible in Africa, TytoCare has announced its partnership with mPharma, a technology-driven healthcare company building Africa's largest health management organisation. A statement issued by the companies on Wednesday, indicates that the partnership involves the integration of the TytoCare solution into mPharma's telehealth offerings which enables pharmacies to provide patients with enhanced remote care through in-depth, physical examinations. Both companies said the partnership will improve health care services to patients in Africa. It said the partnership was rolled out in June 2021 and that since then, over 8,000 people have been examined and treated by mPharma using TytoCare's platform. This spans around 35 pharmacies across Ghana, Kenya, Uganda, Zambia, and Nigeria, the statement said, adding that the partnership will provide solace to patients on the continent due to lack of adequate health care facilities in some countries in the region. Majority of the Primary Health Centres (PHCs) in Nigeria are either abandoned or providing very limited services due to inadequate manpower.

Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics. Outdoor SLAM has been widely used with the assistance of LiDAR or GPS. For indoor applications, the LiDAR technique does not satisfy the accuracy requirement and the GPS signals will be lost. An accurate and efficient scene sensing technique is required for indoor SLAM. As the most promising 3D sensing technique, the opportunities for indoor SLAM with fringe projection profilometry (FPP) systems are obvious, but methods to date have not fully leveraged the accuracy and speed of sensing that such systems offer. In this paper, we propose a novel FPP-based indoor SLAM method based on the coordinate transformation relationship of FPP, where the 2D-to-3D descriptor-assisted is used for mapping and localization. The correspondences generated by matching descriptors are used for fast and accurate mapping, and the transform estimation between the 2D and 3D descriptors is used to localize the sensor. The provided experimental results demonstrate that the proposed indoor SLAM can achieve the localization and mapping accuracy around one millimeter.

Initial work on variational autoencoders assumed independent latent variables with simple distributions. Subsequent work has explored incorporating more complex distributions and dependency structures: including normalizing flows in the encoder network allows latent variables to entangle non-linearly, creating a richer class of distributions for the approximate posterior, and stacking layers of latent variables allows more complex priors to be specified for the generative model. This work explores incorporating arbitrary dependency structures, as specified by Bayesian networks, into VAEs. This is achieved by extending both the prior and inference network with graphical residual flows - residual flows that encode conditional independence by masking the weight matrices of the flow's residual blocks. We compare our model's performance on several synthetic datasets and show its potential in data-sparse settings.

Organized by Divine Fuh, HUMA – Institute for Humanities in Africa at the University of Cape Town, South Africa and Fanny Chabrol, CEPED-IRD, France and funded by Carnegie Corporation of New York and the Wenner-Gren Foundation, this workshop is located within the framework of the project Future Hospitals: 4IR/AI and the Ethics of Care at HUMA – Institute for Humanities in Africa headed by Divine Fuh, and the "Hospital Multiple" at CEPED-IRD headed by Fanny Chabrol. The workshop aims at proposing new ethnographic methodological and conceptual tools to think and imagine the "hospital of the future" in Africa, in particular, the way artificial intelligence (AI) seeks to transform and is currently transforming access to health care in hospitals today and in the coming years. Our project aims to build a problematisation of the hospital of the future and an ethnographic method to critically analyse the ethical, regulatory, and political issues with respect to AI, healthcare, and hospitals on the continent. We consider the "hospital of the future" – through the digitalization and computer automation of healthcare – as a global promise that needs to be challenged by ethnographic methods within hospitals, engaging with persons interacting with them. The first line of inquiry will challenge the logic of adoption and Africa as a place where development policies are implemented, where infrastructure projects are developed, in which technological innovation, mainly coming from the West, is presented as the promise of better health for those in need.

The global data and intelligence solutions provider, Provenir, is leading the marketplace through its data insights innovation and technologies. The US-based software technology company which supports the international fintech industry, ensures the marketplace is a global data and intelligence ecosystem that makes accessing data fast and easy. Now, Provenir has invited industry professionals to join them in their latest webinar that outline how can AI-powered risk decisioning can play a part in transforming the entire credit risk decisioning process. The session, which is presented by key industry leaders, explores how technology continues to evolve and advances in big data, digital transformation, and AI/ML are creating new opportunities for financial services and fintechs to improve their credit decisioning processes. The webinar panel discussion is being moderated by FinTech Magazine and will provide a spectrum of topics for discussion that outline the importance of using AI/ML to transform credit risk decisioning.

For a long time, it was an accepted truth that video games just didn't work on screen. Remember the quasi-cyberpunk 1993 Super Mario movie, starring Dennis Hopper? It was so bad that basically everyone involved with it has disavowed it. And TV? Kids of the 90s will remember the incredibly annoying voice of Sonic the Hedgehog on Saturday morning TV – or the permanent repeats of the Pokémon anime series – but other than that, the entertainment world never took games seriously. Now, though, things are different.

Healthy Soil Biomes (HSB) and StoryFile have launched an AI-powered conversational video experience with five experts to provide the public with engaging resources on how to create healthy farming methods. Just in time for Earth Day, these methods can help mitigate global issues like food/water scarcity and climate change. For the first time, StoryFile has networked multiple people's StoryFiles and utilized its powerful AI-tool, Conversa, to let users have a conversation that can move between five Healthy Soil Biomes experts according to their area of expertise in areas such as bioreactors, farming, soil, and biodiversity. This revolutionary technology is the basis of Video 3.0, which allows for interactive asynchronous conversational video. Over 7,000 questions were asked of the Healthy Soil Biomes experts.