A Role-Based Approach for Orchestrating Emergent Configurations in the Internet of Things

The Internet of Things (IoT) is envisioned as a global network of connected things enabling ubiquitous machine-to-machine (M2M) communication. With estimations of billions of sensors and devices to be connected in the coming years, the IoT has been advocated as having a great potential to impact the way we live, but also how we work. However, the connectivity aspect in itself only accounts for the underlying M2M infrastructure. In order to properly support engineering IoT systems and applications, it is key to orchestrate heterogeneous 'things' in a seamless, adaptive and dynamic manner, such that the system can exhibit a goal-directed behaviour and take appropriate actions. Yet, this form of interaction between things needs to take a user-centric approach and by no means elude the users' requirements. To this end, contextualisation is an important feature of the system, allowing it to infer user activities and prompt the user with relevant information and interactions even in the absence of intentional commands. In this work we propose a role-based model for emergent configurations of connected systems as a means to model, manage, and reason about IoT systems including the user's interaction with them. We put a special focus on integrating the user perspective in order to guide the emergent configurations such that systems goals are aligned with the users' intentions. We discuss related scientific and technical challenges and provide several uses cases outlining the concept of emergent configurations.Comment: In Proceedings of the Second International Workshop on the Internet of Agents @AAMAS201

A Hybrid Travel Recommender System for Group Tourists

Travel recommender systems (TRSs) are developed as information filtering tools to provide travel decision-making support. They make personalised recommendations based on the user’s preferences. People tend to make group travel decisions based on trip-specific motivations. The current Group Travel Recommender Systems (GTRSs) exploit individual user’s preferences and make group recommendations by aggregating profiles or aggregating recommendations. Although aggregation is a straightforward way to combine the preferences of different group members, it has been critiqued on overlooking of the group dynamics. Interaction needs among tourists’ have a great influence on group travel preference. This proposed study explores a conceptual framework for a hybrid group travel recommender system based on this consideration

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

A framework for understanding and predicting the take up and use of social networking tools in a collaborative envionment

Online collaborative environments, such as social networking environments, enable users to work together to create, modify, and share media collaboratively. However, as users can be autonomous in their actions the ability to create and form a shared understanding of the people, purpose, and process of the collaborative effort can be complex. This complexity is compounded by the natural implicit social and collaborative structure of these environments, a structure that can be modified by users dynamically and asynchronously. Some have tried to make this implicitness explicit through data mining, and allocation of user roles. However such methods can fail to adapt to the changing nature of an environment's structure relating to habits of users and their social connectedness. As a result, existing methods generally provide only a snapshot of the environment at a point in time. In addition, existing methods focus on whole user bases and the underlying social context of the environment. This makes them unsuitable for situations where the context of collaboration can change rapidly, for example the tools and widgets available for collaborative action and the users available for collaborative interactions. There is a pre-existing model for understanding the dynamic structure of these environments called the “Group Socialisation Model". This model has been used to understand how social group roles form and change over time as they go through a life cycle. This model also contains a concept of characteristic behaviours or descriptors of behaviour that an individual can use to make judgement about another individual and to create an understanding of a role or social norm that may or may not be explicit. Although studies have used components of this model to provide a means of role identification or role composition within online collaborative environments, they have not managed to provide a higher level method or framework that can replicate the entire life cycle continuously over time within these environments. Using the constructive research methodology this thesis presents a research construct in the form of a framework for replicating the social group role life cycle within online collaborative environments. The framework uses an artificial neural network with a unique capability of taking snapshots of its network structure. In conjunction with fuzzy logic inference, collaborative role signatures composed of characteristic behaviours can then be determined. In this work, three characteristic behaviours were identified from the literature for characterisation of stereotypical online behaviour to be used within a role signature: these were publisher, annotator, and lurker. The use of the framework was demonstrated on three case studies. Two of the case studies were custom built mobile applications specifically for this study, and one was the Walk 2.0 website from a National Health and Medical Research Council project. All three case studies allowed for collaborative actions where users could interact with each other to create an dynamic and diverse environment. For the use of these case studies, ethics was approved by the Western Sydney University Human Research Ethic Committee and consistent strategies for recruitment were carried out. The framework was thereby demonstrated to be capable of successfully determining role signatures composed of the above characteristic behaviours, for a range of contexts and individual users. Also, comparison of participant usage of case studies was carried out and it was established that the role signatures determined by the framework matched usage. In addition, the top contributors within the case studies were analysed to demonstrate the framework's capability of handling the dynamic and continual changing structure of an online collaborative environment. The major contribution of this thesis is a framework construct developed to propose and demonstrate a new framework approach to successfully automate and carry out the social group role model life cycle within online collaborative environments. This is a significant component of foundational work towards providing designers of online collaborative environments with the capacity of understanding the various implicit roles and their characteristic behaviours for individual users. Such a capability could enable more specific individual personalisation or resource allocation, which could in turn improve the suitability of environments developed for collaboration online

The Four Elements of a viable PLE

In this paper, we propose and discuss four fitness features considered as essential for developing personal learning environments (PLE) that are viable and ready for appropriation

Advances in Technology Enhanced Learning

‘Advances in Technology Enhanced Learning’ presents a range of research projects which aim to explore how to make engagement in learning (and teaching) more passionate. This interactive and experimental resource discusses innovations which pave the way to open collaboration at scale. The book introduces methodological and technological breakthroughs via twelve chapters to learners, instructors, and decision-makers in schools, universities, and workplaces. The Open University's Knowledge Media Institute and the EU TELMap project have brought together the luminaries from the European research area to showcase their vision of the future of learning with technology via their recent research project work. The projects discussed range widely over the Technology Enhanced Learning area from: environments for responsive open learning, work-based reflection, work-based social creativity, serious games and many more

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given