The plethora of cloud application services (Apps) in the cloud business apps e-marketplace often leads to service choice overload. Meanwhile, existing SaaS e-marketplaces employ keyword-based inputs that do not consider both the quantitative and qualitative quality of service (QoS) attributes that characterise cloud-based services. Also, existing QoS-based cloud service ranking approaches rank cloud application services are based on the assumption that the services are characterised by quantitative QoS attributes alone, and have employed quantitative-based similarity metrics for ranking. However, the dimensions of cloud service QoS requirements are heterogeneous in nature, comprising both quantitative and qualitative QoS attributes, hence a cloud service ranking approach that embrace core heterogeneous QoS dimensions is essential in order to engender more objective cloud selection. In this paper, we propose the use of heterogeneous similarity metrics (HSM) that combines quantitative and qualitative dimensions for QoS-based ranking of cloud-based services. By using a synthetically generated cloud services dataset, we evaluated the ranking performance of five HSM using Kendall tau rank coefficient and precision as accuracy metrics benchmarked with one HSM. The results show significant rank order correlation of Heterogeneous Euclidean-Eskin Metric, Heterogeneous Euclidean-Overlap Metric, and Heterogeneous Value Difference Metric with human similarity judgment, compared to other metrics used in the study. Our results confirm the applicability of HSM for QoS ranking of cloud services in cloud service e-marketplace with respect to users' heterogeneous QoS requirements.

No abstract

The higher education landscape in developing countries is faced with many challenges, one of which is high faculty to student ratio. An obvious implication of this is compromise on the quality of classroom engagement. The distractions caused by the not conducive learning space and instructors' inability to elucidate correct feedbacks from students usually lead to poor learning outcomes. Feedback mechanisms that are unobtrusive and efficient in processing large data in real-time are needful to measure quality learning experience in such large classroom settings. With the latest impact of penetration and adoption of internet and mobile technologies in most developing counties, wearable technology is a feasible solution to manage and monitor classroom involvement; as real time student feedback can be integrated in the design and delivery of instruction in and out of the classroom. In this paper, we present state of the art of wearable technology and explored the opportunities of wearable technology in the higher education. Specifically, we presented scenarios in which wearable technology can be employed to understand and analyze physiological signals and emotional responses from learners in real-time; the end result of which would increase the quality of classroom engagement, inspire new pedagogy, drive new trends in peer-to-peer collaborations, and increase the learning outcomes. Moreover, we identified some challenges that may hinder this development such as: inconclusive user studies of wearable technology in developing countries and inadequate infrastructure. Finally, we make appropriate recommendations on how these challenges can be surmounted

No abstract

Abstract:Recently, most companies interact more with their customers through the social media, particularly Facebook and Twitter. This has made large amount of textual data freely available on the internet for competitive intelligence analysis, which is helping reposition more and more companies for better profit. In order to carry out competitive intelligence, financial institutions need to take note of and analyse their competitor's social media sites. This paper, therefore, aims to help the banking industry in Nigeria understand how to perform a social media competitive analysis and transform social media data into knowledge, which will form the foundation for decision-making and internet marketing of such institutions. The study describes an in-depth case study which applies text mining to analyse unstructured text content on Facebook and Twitter sites of the five largest and leading financial institutions (banks) in Nigeria: Zenith Bank, First Bank, United Bank for Africa, Access Bank and GTBank. Analysing the social media content of these institutions will increase their competitive advantage and also lead to more profit for the banking institutions in question. The results obtained from this research showed that text mining is able to reveal uncommon and non-trivial trend for competitive advantage from social media data, and also provide specific recommendations to help banks maximise their competitive edge.

<h1><span>In this work, we designed and developed a Virtual Reality guided tour mobile app for Landmark University farms, LF-ViT. We were motivated by the need to circumvent the problem of bio-security caused by incessant visit to the farm by visitors, tourists or customers. <strong></strong></span></h1><span>The guided tour was implemented using the storytelling technique. Other technical details of the design and implementation process are discussed</span>

Abstract:Recently, most companies interact more with their customers through the social media, particularly Facebook and Twitter. This has made large amount of textual data freely available on the internet for competitive intelligence analysis, which is helping reposition more and more companies for better profit. In order to carry out competitive intelligence, financial institutions need to take note of and analyse their competitor's social media sites. This paper, therefore, aims to help the banking industry in Nigeria understand how to perform a social media competitive analysis and transform social media data into knowledge, which will form the foundation for decision-making and internet marketing of such institutions. The study describes an in-depth case study which applies text mining to analyse unstructured text content on Facebook and Twitter sites of the five largest and leading financial institutions (banks) in Nigeria: Zenith Bank, First Bank, United Bank for Africa, Access Bank and GTBank. Analysing the social media content of these institutions will increase their competitive advantage and also lead to more profit for the banking institutions in question. The results obtained from this research showed that text mining is able to reveal uncommon and non-trivial trend for competitive advantage from social media data, and also provide specific recommendations to help banks maximise their competitive edge.

Most cloud service e-marketplaces incorporate basic features like search and billing but lack more sophisticated elements that optimise users' experience. The cognitive demands of searching for and evaluating multiple cloud SaaS along multiple QoS criteria can be overwhelming, giving rise to what Alvin Toffler called choice overload. There is a need to integrate mechanisms that handles the vagueness that characterises the human decision-making process when finding suitable services. The objective of this paper is to reduce cognitive overload during cloud service selection in e-marketplaces by employing low cognitive demanding tools that leverage the dynamics of human expressions. We proposed a QoS-aware SaaS ranking and selection framework that integrates fuzzy theory and information visualisation for optimal decision-making in cloud e-marketplaces. An illustrative case study of Customer-Relationship-Management-as-a-Service e-marketplace demonstrated the framework's plausibility. The demonstration shows that our framework is a viable approach to rank and select SaaS in cloud e-marketplaces in ABOUT THE AUTHOR