Formal evaluation of persona trustworthiness with EUSTACE.

Personas are useful for considering how users of a system might behave, but problematic when accounting for hidden behaviours not obvious from their descriptions alone. Formal methods can potentially identify such subtleties in interactive systems, but we lack methods for eliciting models from qualitative persona descriptions. We present a framework for eliciting and specifying formal models of persona behaviour that a persona might, in certain circumstances, engage in. We also summarise our preliminary work to date evaluating this framework

The relationship of (perceived) epistemic cognition to interaction with resources on the internet

Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation – including sites visited – and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks

A Survey on Trust Computation in the Internet of Things

Internet of Things defines a large number of diverse entities and services which interconnect with each other and individually or cooperatively operate depending on context, conditions and environments, produce a huge personal and sensitive data. In this scenario, the satisfaction of privacy, security and trust plays a critical role in the success of the Internet of Things. Trust here can be considered as a key property to establish trustworthy and seamless connectivity among entities and to guarantee secure services and applications. The aim of this study is to provide a survey on various trust computation strategies and identify future trends in the field. We discuss trust computation methods under several aspects and provide comparison of the approaches based on trust features, performance, advantages, weaknesses and limitations of each strategy. Finally the research discuss on the gap of the trust literature and raise some research directions in trust computation in the Internet of Things

Gulfs of Expectation: Eliciting and Verifying Differences in Trust Expectations using Personas

Personas are a common tool used in Human Computer Interaction to represent the needs and expectations of a system’s stakeholders, but they are also grounded in large amounts of qualitative data. Our aim is to make use of this data to anticipate the differences between a user persona’s expectations of a system, and the expectations held by its developers. This paper introduces the idea of gulfs of expectation – the gap between the expectations held by a user about a system and its developers, and the expectations held by a developer about the system and its users. By evaluating these differences in expectation against a formal representation of a system, we demonstrate how differences between the anticipated user and developer mental models of the system can be verified. We illustrate this using a case study where persona characteristics were analysed to identify divergent behaviour and potential security breaches as a result of differing trust expectations

A Scalable Model for Secure Multiparty Authentication

Distributed system architectures such as cloud computing or the emergent architectures of the Internet Of Things, present significant challenges for security and privacy. Specifically, in a complex application there is a need to securely delegate access control mechanisms to one or more parties, who in turn can govern methods that enable multiple other parties to be authenticated in relation to the services that they wish to consume. We identify shortcomings in an existing proposal by Xu et al for multiparty authentication and evaluate a novel model from Al-Aqrabi et al that has been designed specifically for complex multiple security realm environments. The adoption of a Session Authority Cloud ensures that resources for authentication requests are scalable, whilst permitting the necessary architectural abstraction for myriad hardware IoT devices such as actuators and sensor networks, etc. In addition, the ability to ensure that session credentials are confirmed with the relevant resource principles means that the essential rigour for multiparty authentication is established

Secure and Trusted Execution:Past, Present, and Future - A Critical Review in the Context of the Internet of Things and Cyber-Physical Systems

International audienc

An extended information system success model for mobile learning usage in Saudi Arabia universities

The emergence of 4G networks allows m-learning to be attractive for educational systems. Mobile devices have the potential to enhance accessibility and efficiency distribution of educational materials and information. Developing countries, especially in the Middle East, lag behind as they face difficulties in the adoption and use of m-learning. Previous researches stated that the studies in the success of m-learning are still insufficient in developing countries, particularly in Saudi Arabia where the number of students involved in m-learning also constitutes low percentages. Nine factors that influence the success of m-learning are incorporated and evaluated into a research model. A quantitative approach was used, where questionnaires were sent to three universities in KSA. The contributing factors and the relationships between them were evaluated using a Structural Equation Modelling technique. The research revealed that information quality, user satisfaction (US), trust in technology, attitude, organisation support, trust in organisation, and the net benefits of m-learning positively influence m-learning usage. In addition, the results confirmed that user satisfaction is positively affected by system quality (SEQ), service quality (SQ), and net benefits (NB) of using (U) the system. The results also showed that there is a significant relationship between NB and US for m-learning technology. This study extends the previous research by providing a conceptual model for the successful execution of m-learning services in universities. This mediating effect of US explains the impact of independent variables (IQ, SEQ, SQ) on U. It also examined the mediating effect of U in explaining the influence of US on the NB using m-learning services. The findings of this study are valuable as input for the Ministry of Higher Education and practitioners concerned with successful m-learning services. This study constructed a new model to enhance the mobile learning usage among students in universities