27,427 research outputs found
Models of everywhere revisited: a technological perspective
The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the
environmental science of a place, changing the nature of the underlying modelling process, from one in which
general model structures are used to one in which modelling becomes a learning process about specific places, in
particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another
it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere,
models of everything and models at all times, being constantly re-evaluated against the most current
evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities.
However, the approach has, as yet, not been fully utilised or explored. This paper examines the
concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first
proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud
computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again
at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the
remaining research questions. The paper concludes by identifying the key elements of a research agenda that
should underpin such experimentation and deployment
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Surveying Persons with Disabilities: A Source Guide (Version 1)
As a collaborator with the Cornell Rehabilitation Research and Training Center on Disability Demographics and Statistics, Mathematica Policy Research, Inc. has been working on a project that identifies the strengths and limitations in existing disability data collection in both content and data collection methodology. The intended outcomes of this project include expanding and synthesizing knowledge of best practices and the extent existing data use those practices, informing the development of data enhancement options, and contributing to a more informed use of existing data. In an effort to provide the public with an up-to-date and easily accessible source of research on the methodological issues associated with surveying persons with disabilities, MPR has prepared a Source Guide of material related to this topic. The Source Guide contains 150 abstracts, summaries, and references, followed by a Subject Index, which cross references the sources from the Reference List under various subjects. The Source Guide is viewed as a “living document,” and will be periodically updated
A data taxonomy for adaptive multifactor authentication in the internet of health care things
The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The systems of interconnected devices, users, data, and working environments are referred to as the Internet of Health Care Things (IoHT). IoHT devices have emerged in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources. These devices cater to the need for remote health care services outside of physical interactions. However, IoHT security is often overlooked because the devices are quickly deployed and configured as solutions to meet the demands of a heavily saturated industry. During the COVID-19 pandemic, studies have shown that cybercriminals are exploiting the health care industry, and data breaches are targeting user credentials through authentication vulnerabilities. Poor password use and management and the lack of multifactor authentication security posture within IoHT cause a loss of millions according to the IBM reports. Therefore, it is important that health care authentication security moves toward adaptive multifactor authentication (AMFA) to replace the traditional approaches to authentication. We identified a lack of taxonomy for data models that particularly focus on IoHT data architecture to improve the feasibility of AMFA. This viewpoint focuses on identifying key cybersecurity challenges in a theoretical framework for a data model that summarizes the main components of IoHT data. The data are to be used in modalities that are suited for health care users in modern IoHT environments and in response to the COVID-19 pandemic. To establish the data taxonomy, a review of recent IoHT papers was conducted to discuss the related work in IoHT data management and use in next-generation authentication systems. Reports, journal articles, conferences, and white papers were reviewed for IoHT authentication data technologies in relation to the problem statement of remote authentication and user management systems. Only publications written in English from the last decade were included (2012-2022) to identify key issues within the current health care practices and their management of IoHT devices. We discuss the components of the IoHT architecture from the perspective of data management and sensitivity to ensure privacy for all users. The data model addresses the security requirements of IoHT users, environments, and devices toward the automation of AMFA in health care. We found that in health care authentication, the significant threats occurring were related to data breaches owing to weak security options and poor user configuration of IoHT devices. The security requirements of IoHT data architecture and identified impactful methods of cybersecurity for health care devices, data, and their respective attacks are discussed. Data taxonomy provides better understanding, solutions, and improvements of user authentication in remote working environments for security features
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