Evaluating empowerment and control of HE e-learning in a secure environment

With the increased spread of HE distance learning into a wide variety of contexts it is important for us to understand the factors involved in its successful deployment for students. E-learning has a great potential to support effective and empowering HE distance learning (Wilson, 2007; Adams, 2005; Hughes, 2005). However, within two secure environments, prisons and health service, the factors involved are complex. This paper reviews HE e-learning technology perceptions within these two contrasting contexts from 225 students' and stakeholders' perspectives. Previous research has detailed literature limitations on obtaining students' perspectives of e-learning (Conole et al, 2006). These limitations are compounded when other stakeholder perceptions are not integrated (Sun et al, 2007; Adams et al, 2005; Millen at al, 2002). This paper developed and applied an e-learning framework for student and stakeholder perceptions. This social psychological framework, is based on previous practice based e-learning studies and is used here to synthesise two large-scale case studies. The framework focuses on three concepts learner Access (e.g. learning design, technology design, physical access), Awareness (e.g. of resources, their usage and support for e-learning tasks) and Acceptability (e.g. trust, privacy, aesthetics, engagement). Students' and stakeholders' perceptions identified high levels of students' empowerment through e-learning whilst still requiring a further pedagogical tailoring and an awareness of support. However, serious problems within these contexts have identified blocks to e-learning through stakeholders perceptions and fears of acceptability (i.e. issues of risk and trust). Ultimately, through understanding competing perceptions and needs within these complex environments we can support the effective technological development, pedagogical design and deployment of e-learning systems

Dynamic User Role Assignment in Remote Access Control

The Role-Based Access Control (RBAC) model has been widely applied to a single domain in which users are known to the administrative unit of that domain, beforehand. However, the application of the conventional RBAC model for remote access control scenarios is not straightforward. In such scenarios, the access requestor is outside of the provider domain and thus, the user population is heterogeneous and dynamic. Here, the main challenge is to automatically assign users to appropriate roles of the provider domain. Trust management has been proposed as a supporting technique to solve the problem of remote access control. The key idea is to establish a mutual trust between the requestor and provider based on credentials they exchange. However, a credential doesn't convey any information about the behavior of its holder during the time it is being used. Furthermore, in terms of privileges granted to the requestor, existing trust management systems are either too restrictive or not restrictive enough. In this paper, we propose a new dynamic user-role assignment approach for remote access control, where a stranger requests for access from a provider domain. Our approach has two advantages compared to the existing dynamic user-role assignment techniques. Firstly, it addresses the principle of least privilege without degrading the efficiency of the access control system. Secondly, it takes into account both credentials and the past behavior of the requestor in such a way that he cannot compensate for the lack of necessary credentials by having a good past behavior

Implanting Life-Cycle Privacy Policies in a Context Database

Ambient intelligence (AmI) environments continuously monitor surrounding individuals' context (e.g., location, activity, etc.) to make existing applications smarter, i.e., make decision without requiring user interaction. Such AmI smartness ability is tightly coupled to quantity and quality of the available (past and present) context. However, context is often linked to an individual (e.g., location of a given person) and as such falls under privacy directives. The goal of this paper is to enable the difficult wedding of privacy (automatically fulfilling users' privacy whishes) and smartness in the AmI. interestingly, privacy requirements in the AmI are different from traditional environments, where systems usually manage durable data (e.g., medical or banking information), collected and updated trustfully either by the donor herself, her doctor, or an employee of her bank. Therefore, proper information disclosure to third parties constitutes a major privacy concern in the traditional studies

How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed