Social Navigation of Food Recipes

The term Social Navigation captures every-day behaviour used to find information, people, and places – namely through watching, following, and talking to people. We discuss how to design information spaces to allow for social navigation. We applied our ideas in a recipe recommendation system. In a follow-up user study, subjects state that social navigation adds value to the service: it provides for social affordance, and it helps turning a space into a social place. The study also reveals some unresolved design issues, such as the snowball effect where more and more users follow each other down the wrong path, and privacy issues

Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites

Apache Mahout’s k-Means vs. fuzzy k-Means performance evaluation

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The emergence of the Big Data as a disruptive technology for next generation of intelligent systems, has brought many issues of how to extract and make use of the knowledge obtained from the data within short times, limited budget and under high rates of data generation. The foremost challenge identified here is the data processing, and especially, mining and analysis for knowledge extraction. As the 'old' data mining frameworks were designed without Big Data requirements, a new generation of such frameworks is being developed fully implemented in Cloud platforms. One such frameworks is Apache Mahout aimed to leverage fast processing and analysis of Big Data. The performance of such new data mining frameworks is yet to be evaluated and potential limitations are to be revealed. In this paper we analyse the performance of Apache Mahout using large real data sets from the Twitter stream. We exemplify the analysis for the case of two clustering algorithms, namely, k-Means and Fuzzy k-Means, using a Hadoop cluster infrastructure for the experimental study.Peer ReviewedPostprint (author's final draft

Who you’re gonna call? The development of university digital leaders

In our hyper-connected digitised educational world, university tutors are interested in capitalising on affordances of digital trends in teaching and learning. Students, under the alias of preservice- teachers, walk among them equipped with digital skills in areas of their interest. How can we encourage collaboration between tutors and students that can promote the use of the digital force wisely, support the development of students’ professional identities further and extend tutors’ digital competences? The story of nine tutors and eleven undergraduate pre-service-teachers working together on digital partnerships is set against discussions around digital leadership and citizenship. This case study aims to highlight how universities can respond to technology-driven change by engaging students further and support their awareness of digital citizenship. The overall results showed that the informal learning that students have capitalised outside the classroom can be used to scaffold their development of digital citizenship through offline community engagement. It demonstrates the advantage of using such opportunities as a means to encourage citizenship practices among university student communities and the positive impact that such synergies can have on all the participants

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Securing the Elderly: A Developmental Approach to Hypermedia-Based Online Information Security for Senior Novice Computer Users

Whilst security threats to the general public continue to evolve, elderly computer users with limited skill and knowledge are left playing catch-up in an ever-widening gap in fundamental cyber-related comprehension. As a definable cohort, the elderly generally lack awareness of current security threats, and remain under-educated in terms of applying appropriate controls and safeguards to their computers and networking devices. This paper identifies that web-based computer security information sources do not adequately provide helpful information to senior citizen end-users in terms of both design and content

Issues for the sharing and re-use of scientific workflows

In this paper, we outline preliminary findings from an ongoing study we have been conducting over the past 18 months of researchers’ use of myExperiment, a Web 2.0-based repository with a focus on social networking around shared research artefacts such as workflows. We present evidence of myExperiment users’ workflow sharing and re-use practices, motivations, concerns and potential barriers. The paper concludes with. a discussion of the implications of these our findings for community formation, diffusion of innovations, emerging drivers and incentives for research practice, and IT systems design