This article was originally published on our sister site, Freethink. A financial consulting firm has created AI avatars for its staff, which they can use to quickly create deepfakes of themselves for presentations, emails, and more. The challenge: During the pandemic, remote work became the norm at many companies, and meetings that might have once taken place over lunch happened over the internet instead. This transition was more difficult for some industries than others, and those that traditionally relied on face-time with clients to build relationships and secure deals may have struggled to find their footing. "[W]hile much has been written about how to collaborate remotely with coworkers … companies still are trying to figure out the best way to connect with clients over teleconferencing platforms," Snjezana Cvoro-Begovic and James Hartling, execs at the software company Cognizant Softvision, wrote in Fast Company.
A financial consulting firm has created AI avatars for its staff, which they can use to quickly create deepfakes of themselves for presentations, emails, and more. The challenge: During the pandemic, remote work became the norm at many companies, and meetings that might have once taken place over lunch happened over the internet instead. This transition was more difficult for some industries than others, and those that traditionally relied on face-time with clients to build relationships and secure deals may have struggled to find their footing. "As opposed to sending an email and saying'Hey we're still on for Friday,' you can see me and hear my voice." "[W]hile much has been written about how to collaborate remotely with coworkers … companies still are trying to figure out the best way to connect with clients over teleconferencing platforms," Snjezana Cvoro-Begovic and James Hartling, execs at the software company Cognizant Softvision, wrote in Fast Company.
Health records data security is one of the main challenges in e-health systems. Authentication is one of the essential security services to support the stored data confidentiality, integrity, and availability. This research focuses on the secure storage of patient and medical records in the healthcare sector where data security and unauthorized access is an ongoing issue. A potential solution comes from biometrics, although their use may be time-consuming and can slow down data retrieval. This research aims to overcome these challenges and enhance data access control in the healthcare sector through the addition of biometrics in the form of fingerprints. The proposed model for application in the healthcare sector consists of Collection, Network communication, and Authentication (CNA) using biometrics, which replaces an existing password-based access control method. A sensor then collects data and by using a network (wireless or Zig-bee), a connection is established, after connectivity analytics and data management work which processes and aggregate the data. Subsequently, access is granted to authenticated users of the application. This IoT-based biometric authentication system facilitates effective recognition and ensures confidentiality, integrity, and reliability of patients, records and other sensitive data. The proposed solution provides reliable access to healthcare data and enables secure access through the process of user and device authentication. The proposed model has been developed for access control to data through the authentication of users in healthcare to reduce data manipulation or theft.
Edge technology aims to bring Cloud resources (specifically, the compute, storage, and network) to the closed proximity of the Edge devices, i.e., smart devices where the data are produced and consumed. Embedding computing and application in Edge devices lead to emerging of two new concepts in Edge technology, namely, Edge computing and Edge analytics. Edge analytics uses some techniques or algorithms to analyze the data generated by the Edge devices. With the emerging of Edge analytics, the Edge devices have become a complete set. Currently, Edge analytics is unable to provide full support for the execution of the analytic techniques. The Edge devices cannot execute advanced and sophisticated analytic algorithms following various constraints such as limited power supply, small memory size, limited resources, etc. This article aims to provide a detailed discussion on Edge analytics. A clear explanation to distinguish between the three concepts of Edge technology, namely, Edge devices, Edge computing, and Edge analytics, along with their issues. Furthermore, the article discusses the implementation of Edge analytics to solve many problems in various areas such as retail, agriculture, industry, and healthcare. In addition, the research papers of the state-of-the-art edge analytics are rigorously reviewed in this article to explore the existing issues, emerging challenges, research opportunities and their directions, and applications.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
In the last five years, edge computing has attracted tremendous attention from industry and academia due to its promise to reduce latency, save bandwidth, improve availability, and protect data privacy to keep data secure. At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services. These two trends, combined together, have created a new horizon: Edge Intelligence (EI). The development of EI requires much attention from both the computer systems research community and the AI community to meet these demands. However, existing computing techniques used in the cloud are not applicable to edge computing directly due to the diversity of computing sources and the distribution of data sources. We envision that there missing a framework that can be rapidly deployed on edge and enable edge AI capabilities. To address this challenge, in this paper we first present the definition and a systematic review of EI. Then, we introduce an Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability. We analyze four fundamental EI techniques which are used to build OpenEI and identify several open problems based on potential research directions. Finally, four typical application scenarios enabled by OpenEI are presented.
For healthcare professionals, this requires the clearest x-ray images possible to facilitate fast and accurate diagnosis of patients. Drawing on longstanding experience in the development of radiology and imaging solutions, technologies delivered by Thales provide hospitals with exceptional image quality, rapid image acquisition and processing, coupled with connectivity capabilities that enable instant clinical data transmission. New-generation solutions go further by capitalizing on synergies in artificial intelligence, cybersecurity, big data and data management, and the Internet of Things. "Our ongoing research that looks to embed artificial intelligence and deep learning into our solutions allows for a better diagnosis so that the patient is treated quickly and correctly," says Inès Mouga, Strategy & Innovation Director. "We provide healthcare professionals with unparalleled visual support through the sharpest-ever radiological image detection and interpretation solutions. With increasing amounts of data flowing given the digitalization of the health sector, we are also providing customers with the innovative cybersecurity solutions they need to protect their radiology systems."