The simplicity project: easing the burden of using complex and heterogeneous ICT devices and services

As of today, to exploit the variety of different "services", users need to configure each of their devices by using different procedures and need to explicitly select among heterogeneous access technologies and protocols. In addition to that, users are authenticated and charged by different means. The lack of implicit human computer interaction, context-awareness and standardisation places an enormous burden of complexity on the shoulders of the final users. The IST-Simplicity project aims at leveraging such problems by: i) automatically creating and customizing a user communication space; ii) adapting services to user terminal characteristics and to users preferences; iii) orchestrating network capabilities. The aim of this paper is to present the technical framework of the IST-Simplicity project. This paper is a thorough analysis and qualitative evaluation of the different technologies, standards and works presented in the literature related to the Simplicity system to be developed

A linked data compliant framework for dynamic and web-scale consumption of web services

The While Semantic Web Services (SWS) research aims at automating Web service tasks such as discovery, orchestration and execution, its take-up is very limited so far. This is due to several reasons, such as inherent complexity of existing SWS frameworks and the considerable costs involved in creating correct SWS descriptions. In addition, while semantics are in use to enable tasks such as discovery, interaction between service consumers, providers and brokering environments is still not supported by semantic message descriptions. On the other hand, the Linked Data approach has produced a set of established principles for sharing and describing data, such as RDF as representation language and the integral use of dereferencable URIs. In this paper we propose to apply those principles to expose Web services and Web APIs and introduce a framework in which service registries as well as services contribute to the automation of service discovery, and hence, workload is distributed more efficiently. This is achieved by developing a Linked Data compliant Web services framework with that communicate with semi-centralised registries but compute their suitability for a given request themselves. All communications among different framework components are using RDF-based message protocols including service input and output. This framework aims at optimizing load balance and performance by dynamically assembling services at run time in a massively distributed Web environment

The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio

Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

Naive Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages

Web classification has been attempted through many different technologies. In this study we concentrate on the comparison of Neural Networks (NN), Naïve Bayes (NB) and Decision Tree (DT) classifiers for the automatic analysis and classification of attribute data from training course web pages. We introduce an enhanced NB classifier and run the same data sample through the DT and NN classifiers to determine the success rate of our classifier in the training courses domain. This research shows that our enhanced NB classifier not only outperforms the traditional NB classifier, but also performs similarly as good, if not better, than some more popular, rival techniques. This paper also shows that, overall, our NB classifier is the best choice for the training courses domain, achieving an impressive F-Measure value of over 97%, despite it being trained with fewer samples than any of the classification systems we have encountered