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  • 1.
    Abu Khousa, Eman
    et al.
    UAE University, Al Ain, United Arab Emirates.
    Atif, Yacine
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Social network analysis to influence career development2018In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 9, no 3, p. 601-616Article in journal (Refereed)
    Abstract [en]

    Social network analysis techniques have shown a potential for influencing gradu-ates to meet industry needs. In this paper, we propose a social-web driven solutions to bridge formal education and industry needs. The proposed career development frame-work utilizes social network analytics, influence diffusion algorithms and persuasive technology models along three phases: (1) career readiness to measure and visualize the general cognitive dispositions required for a successful career in the 21st Century, (2) career prediction to persuade future graduates into a desired career path by clustering learners whose career prospects are deemed similar, into a community of practice; and (3) career development to drive growth within a social network structure where social network analytics and persuasive techniques are applied to incite the adoption of desired career behaviors. The process starts by discovering behavioral features to create a cognitive profile and diagnose individual deficiencies. Then, we develop a fuzzy clustering algorithm that predicts similar patterns with controlled constraint-violations to construct a social structure for collaborative cognitive attainment. This social framework facilitates the deployment of novel influence diffusion approaches, whereby we propose a reciprocal-weighted similarity function and a triadic clo-sure approach. In doing so, we investigate contemporary social network analytics to maximize influence diffusion across a synthesized social network. The outcome of this social computing approach leads to a persuasive model that supports behavioral changes and developments. The performance results obtained from both analytical and experi-mental evaluations validate our data-driven strategy for persuasive behavioral change.

  • 2.
    Atif, Yacine
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Al-Falahi, Kanna
    College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates.
    Wangchuk, Tshering
    Royal Institute of Management, Thimphu, Bhutan.
    Lindström, Birgitta
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    A fuzzy logic approach to influence maximization in social networks2019In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145Article in journal (Refereed)
    Abstract [en]

    Within a community, social relationships are paramount to profile individuals’ conduct. For instance, an individual within a social network might be compelled to embrace a behaviour that his/her companion has recently adopted. Such social attitude is labelled social influence, which assesses the extent by which an individual’s social neighbourhood adopt that individual’s behaviour. We suggest an original approach to influence maximization using a fuzzy-logic based model, which combines influence-weights associated with historical logs of the social network users, and their favourable location in the network. Our approach uses a two-phases process to maximise influence diffusion. First, we harness the complexity of the problem by partitioning the network into significantly-enriched community-structures, which we then use as modules to locate the most influential nodes across the entire network. These key users are determined relatively to a fuzzy-logic based technique that identifies the most influential users, out of which the seed-set candidates to diffuse a behaviour or an innovation are extracted following the allocated budget for the influence campaign. This way to deal with influence propagation in social networks, is different from previous models, which do not compare structural and behavioural attributes among members of the network. The performance results show the validity of the proposed partitioning-approach of a social network into communities, and its contribution to “activate” a higher number of nodes overall. Our experimental study involves both empirical and real contemporary social-networks, whereby a smaller seed set of key users, is shown to scale influence to the high-end compared to some renowned techniques, which employ a larger seed set of key users and yet they influence less nodes in the social network.

  • 3.
    Atif, Yacine
    et al.
    UAE University, United Arab Emirates.
    Mathew, Sujith
    UAE University, United Arab Emirates.
    Lakas, Abderahmane
    UAE University, United Arab Emirates.
    Building a smart campus to support ubiquitous learning2014In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 6, no 2, p. 223-238Article in journal (Refereed)
    Abstract [en]

    New technological advances in user mobility and context immersion are enabling novel adaptive and pervasive learning models in ambient environments. These advances allow physical learning spaces with embedded computing capabilities to provide an augmented self-aware learning experience. In this paper, we aim at developing a novel ubiquitous learning model within a pervasive smart campus environment. The goal of our research consists of identifying the steps towards building such an environment and the involved learning processes. We define a model of a smart campus, and advocate learning practices in the light of new paradigms such as context-awareness, ubiquitous learning, pervasive environment, resource virtualization, autonomic computing and adaptive learning. We reveal a comprehensive architecture that defines the various components and their inter-operations in a smart educational environment. The smart campus approach is presented as a composition of ambient learning spaces, which are environments where physical learning resources are augmented with digital and social services. We present a model of these spaces to harness future ubiquitous learning environments. One of the distinguished features of this model is the ability to unleash the instructional value of surrounding physical structures. Another one is the provision of a personalized learning agenda when moving across these ambient learning environments. To achieve these goals, we profile learners and augment physical campus structures to advocate context-aware learning processes. We suggest a social community platform for knowledge sharing which involves peer learners, domain experts as well as campus physical resources. Within this pervasive social scope, learners are continuously immersed in a pedagogically supported experiential learning loop as a persuasive approach to learning. A learning path, which responds to learners’ goals and qualifications, autonomously guides learners in achieving their objectives in the proposed smart campus. We evaluated our ubiquitous learning approach to assert the performance of these building blocks in the proposed smart campus model. The results show interesting tradeoffs and promising insights.

  • 4.
    Steinhauer, H. Joe
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Helldin, Tove
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Mathiason, Gunnar
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Topic modeling for anomaly detection in telecommunication networks2019In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, p. 1-12Article in journal (Refereed)
    Abstract [en]

    To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the results of an initial experiment for anomaly detection with regard to network performance, using topic modeling on base station run-time variable data collected from live Radio Access Networks (RANs). The results show that topic modeling clusters semantically related data in the same way as human experts would and that the anomalies in our test cases could be identified in latent Dirichlet allocation (LDA) topic models. Our experiment further reveals which information provided by the topic model is particularly usable to support human anomaly detection in this application domain.

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