Context Aware Middleware Architectures: Survey and Challenges

Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work

Enhanced ontology-based text classification algorithm for structurally organized documents

Text classification (TC) is an important foundation of information retrieval and text mining. The main task of a TC is to predict the text‟s class according to the type of tag given in advance. Most TC algorithms used terms in representing the document which does not consider the relations among the terms. These algorithms represent documents in a space where every word is assumed to be a dimension. As a result such representations generate high dimensionality which gives a negative effect on the classification performance. The objectives of this thesis are to formulate algorithms for classifying text by creating suitable feature vector and reducing the dimension of data which will enhance the classification accuracy. This research combines the ontology and text representation for classification by developing five algorithms. The first and second algorithms namely Concept Feature Vector (CFV) and Structure Feature Vector (SFV), create feature vector to represent the document. The third algorithm is the Ontology Based Text Classification (OBTC) and is designed to reduce the dimensionality of training sets. The fourth and fifth algorithms, Concept Feature Vector_Text Classification (CFV_TC) and Structure Feature Vector_Text Classification (SFV_TC) classify the document to its related set of classes. These proposed algorithms were tested on five different scientific paper datasets downloaded from different digital libraries and repositories. Experimental obtained from the proposed algorithm, CFV_TC and SFV_TC shown better average results in terms of precision, recall, f-measure and accuracy compared against SVM and RSS approaches. The work in this study contributes to exploring the related document in information retrieval and text mining research by using ontology in TC

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

The Blended Learning Unit, University of Hertfordshire: A Centre for Excellence in Teaching and Learning, Evaluation Report for HEFCE

The University of Hertfordshire’s Blended Learning Unit (BLU) was one of the 74 Centres for Excellence in Teaching and Learning (CETLs) funded by the Higher Education Funding Council for England (HEFCE) between 2005 and 2010. This evaluation report follows HEFCE’s template. The first section provides statistical information about the BLU’s activity. The second section is an evaluative reflection responding to 13 questions. As well as articulating some of our achievements and the challenges we have faced, it also sets out how the BLU’s activity will continue and make a significant contribution to delivery of the University of Hertfordshire’s 2010-2015 strategic plan and its aspirations for a more sustainable future. At the University of Hertfordshire, we view Blended Learning as the use of Information and Communication Technology (ICT) to enhance the learning and learning experience of campus-based students. The University has an excellent learning technology infrastructure that includes its VLE, StudyNet. StudyNet gives students access to a range of tools, resources and support 24/7 from anywhere in the world and its robustness, flexibility and ease of use have been fundamental to the success of the Blended Learning agenda at Hertfordshire. The BLU has comprised a management team, expert teachers seconded from around the University, professional support and a Student Consultant. The secondment staffing model was essential to the success of the BLU. As well as enabling the BLU to become fully staffed within the first five months of the CETL initiative, it has facilitated access to an invaluable spectrum of Blended Learning, research and Change Management expertise to inform pedagogically sound developments and enable change to be embedded across the institution. The BLU used much of its capital funding to reduce barriers to the use of technology by, for example, providing laptop computers for all academic staff in the institution, enhancing classroom technology provision and wirelessly enabling all teaching accommodation. Its recurrent funding has supported development opportunities for its own staff and staff around the institution; supported evaluation activities relating to individual projects and of the BLU’s own impact; and supported a wide range of communication and dissemination activities internally and externally. The BLU has led the embedding a cultural change in relation to Blended Learning at the University of Hertfordshire and its impact will be sustained. The BLU has produced a rich legacy of resources for our own staff and for others in the sector. The University’s increased capacity in Blended Learning benefits all our students and provides a learning experience that is expected by the new generation of learners in the 21st century. The BLU’s staffing model and partnership ways of working have directly informed the structure and modus operandi of the University’s Learning and Teaching Institute (LTI). Indeed a BLU team will continue to operate within the LTI and help drive and support the implementation of the University’s 2010-2015 Strategic plan. The plan includes ambitions in relation to Distance Learning and Flexible learning and BLU will be working to enable greater engagement with students with less or no need to travel to the university. As well as opening new markets within the UK and overseas, even greater flexibility for students will also enable the University to reduce its carbon footprint and provide a multifaceted contribution to our sustainability agenda. We conclude this executive summary with a short paragraph, written by Eeva Leinonen, our former Deputy Vice-Chancellor, which reflects our aspiration to transform Learning and Teaching at the University of Hertfordshire and more widely in the sector. ‘As Deputy Vice Chancellor at Hertfordshire I had the privilege to experience closely the excellent work of the Blended Learning Unit, and was very proud of the enormous impact the CETL had not only across the University but also nationally and internationally. However, perhaps true impact is hard to judge at such close range, but now as Vice Principal (Education) at King's College London, I can unequivocally say that Hertfordshire is indeed considered as the leading Blended Learning university in the sector. My new colleagues at King's and other Russell Group Universities frequently seek my views on the 'Hertfordshire Blended Learning' experience and are keen to emulate the successes achieved at an institutional wide scale. The Hertfordshire CETL undoubtedly achieved not only what it set out to achieve, but much more in terms of scale and impact. All those involved in this success can be justifiably proud of their achievements.’ Professor Eeva Leinonen, Vice Principal (Education), King's College, Londo

Digital 3D Technologies for Humanities Research and Education: An Overview

Digital 3D modelling and visualization technologies have been widely applied to support research in the humanities since the 1980s. Since technological backgrounds, project opportunities, and methodological considerations for application are widely discussed in the literature, one of the next tasks is to validate these techniques within a wider scientific community and establish them in the culture of academic disciplines. This article resulted from a postdoctoral thesis and is intended to provide a comprehensive overview on the use of digital 3D technologies in the humanities with regards to (1) scenarios, user communities, and epistemic challenges; (2) technologies, UX design, and workflows; and (3) framework conditions as legislation, infrastructures, and teaching programs. Although the results are of relevance for 3D modelling in all humanities disciplines, the focus of our studies is on modelling of past architectural and cultural landscape objects via interpretative 3D reconstruction methods