Towards engineering ontologies for cognitive profiling of agents on the semantic web

Research shows that most agent-based collaborations suffer from lack of flexibility. This is due to the fact that most agent-based applications assume pre-defined knowledge of agents’ capabilities and/or neglect basic cognitive and interactional requirements in multi-agent collaboration. The highlight of this paper is that it brings cognitive models (inspired from cognitive sciences and HCI) proposing architectural and knowledge-based requirements for agents to structure ontological models for cognitive profiling in order to increase cognitive awareness between themselves, which in turn promotes flexibility, reusability and predictability of agent behavior; thus contributing towards minimizing cognitive overload incurred on humans. The semantic web is used as an action mediating space, where shared knowledge base in the form of ontological models provides affordances for improving cognitive awareness

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

SPEIR: Scottish Portals for Education, Information and Research. Final Project Report: Elements and Future Development Requirements of a Common Information Environment for Scotland

The SPEIR (Scottish Portals for Education, Information and Research) project was funded by the Scottish Library and Information Council (SLIC). It ran from February 2003 to September 2004, slightly longer than the 18 months originally scheduled and was managed by the Centre for Digital Library Research (CDLR). With SLIC's agreement, community stakeholders were represented in the project by the Confederation of Scottish Mini-Cooperatives (CoSMiC), an organisation whose members include SLIC, the National Library of Scotland (NLS), the Scottish Further Education Unit (SFEU), the Scottish Confederation of University and Research Libraries (SCURL), regional cooperatives such as the Ayrshire Libraries Forum (ALF)1, and representatives from the Museums and Archives communities in Scotland. Aims; A Common Information Environment For Scotland The aims of the project were to: o Conduct basic research into the distributed information infrastructure requirements of the Scottish Cultural Portal pilot and the public library CAIRNS integration proposal; o Develop associated pilot facilities by enhancing existing facilities or developing new ones; o Ensure that both infrastructure proposals and pilot facilities were sufficiently generic to be utilised in support of other portals developed by the Scottish information community; o Ensure the interoperability of infrastructural elements beyond Scotland through adherence to established or developing national and international standards. Since the Scottish information landscape is taken by CoSMiC members to encompass relevant activities in Archives, Libraries, Museums, and related domains, the project was, in essence, concerned with identifying, researching, and developing the elements of an internationally interoperable common information environment for Scotland, and of determining the best path for future progress

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

An open learning environment for the diagnosis, assistance and evaluation of students based on artificial intelligence

The personalized diagnosis, assistance and evaluation of students in open learning environments can be a challenging task, especially in cases that the processes need to be taking place in real-time, classroom conditions. This paper describes the design of an open learning environment under development, designed to monitor the comprehension of students, assess their prior knowledge, build individual learner profiles, provide personalized assistance and, finally, evaluate their performance by using artificial intelligence. A trial test has been performed, with the participation of 20 students, which displayed promising results

On Optimal and Fair Service Allocation in Mobile Cloud Computing

This paper studies the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. We exploit the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. We proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated to mobile service usage patterns. We show that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (73% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high mobile application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing, video streaming and multimedia file sharing applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint and Manhattan models