A Voice-Enabled Framework for Recommender and Adaptation Systems in E-Learning

With the proliferation of learning resources on the Web, finding suitable content (using telephone) has become a rigorous task for voice-based online learners to achieve better performance. The problem with Finding Content Suitability (FCS) with voice E-Learning applications is more complex when the sight-impaired learner is involved. Existing voice-enabled applications in the domain of E-Learning lack the attributes of adaptive and reusable learning objects to be able to address the FCS problem. This study provides a Voice-enabled Framework for Recommender and Adaptation (VeFRA) Systems in E-learning and an implementation of a system based on the framework with dual user interfaces – voice and Web. A usability study was carried out in a visually impaired and non-visually impaired school using the International Standard Organization’s (ISO) 9241-11 specification to determine the level of effectiveness, efficiency and user satisfaction. The result of the usability evaluation reveals that the prototype application developed for the school has “Good Usability” rating of 4.13 out of 5 scale. This shows that the application will not only complement existing mobile and Web-based learning systems, but will be of immense benefit to users, based on the system’s capacity for taking autonomous decisions that are capable of adapting to the needs of both visually impaired and non-visually impaired learners

A review on massive e-learning (MOOC) design, delivery and assessment

MOOCs or Massive Online Open Courses based on Open Educational Resources (OER) might be one of the most versatile ways to offer access to quality education, especially for those residing in far or disadvantaged areas. This article analyzes the state of the art on MOOCs, exploring open research questions and setting interesting topics and goals for further research. Finally, it proposes a framework that includes the use of software agents with the aim to improve and personalize management, delivery, efficiency and evaluation of massive online courses on an individual level basis.Peer ReviewedPostprint (author's final draft

E-voting vs. e-trust: A test bed for e-democracy in a world in crisis?

The world is currently experiencing a number of real challenges in the face of persistent economic crisis, local conflicts and huge waves of refugees. All of the above are affecting secure access to, completion of peoples’voting rights and participation in Democratic processes in their homeland.The relevant technological solutions appear to have matured and have successfully responded to rigorous testing. The need for digital access to election events is today higher than ever in the digital age. The question that remains to be answered is whether all this is enough to inspire people to trust the systems and participate in e-voting; particularly in areas where the political climate might be volatile and Trust is a rare commodity. The authors present the outcome of a survey among the Palestinian diaspora to gauge their trust and willingness to use e-voting systems in the Palestinian Authority’s elections. This work does not consider political implications or even the willingness of a specific government to embrace e-voting for conducting election

Multi-step learning rule for recurrent neural models: an application to time series forecasting

Multi-step prediction is a difficult task that has attracted increasing interest in recent years. It tries to achieve predictions several steps ahead into the future starting from current information. The interest in this work is the development of nonlinear neural models for the purpose of building multi-step time series prediction schemes. In that context, the most popular neural models are based on the traditional feedforward neural networks. However, this kind of model may present some disadvantages when a long-term prediction problem is formulated because they are trained to predict only the next sampling time. In this paper, a neural model based on a partially recurrent neural network is proposed as a better alternative. For the recurrent model, a learning phase with the purpose of long-term prediction is imposed, which allows to obtain better predictions of time series in the future. In order to validate the performance of the recurrent neural model to predict the dynamic behaviour of the series in the future, three different data time series have been used as study cases. An artificial data time series, the logistic map, and two real time series, sunspots and laser data. Models based on feedforward neural networks have also been used and compared against the proposed model. The results suggest than the recurrent model can help in improving the prediction accuracy.Publicad

Impact of Relay Location of STANC Bi-Directional Transmission for Future Autonomous Internet of Things Applications

Wireless communication using existing coding models poses several challenges for RF signals due to multipath scattering, rapid fluctuations in signal strength and path loss effect. Unlike existing works, this study presents a novel coding technique based on Analogue Network Coding (ANC) in conjunction with Space Time Block Coding (STBC), termed as Space Time Analogue Network Coding (STANC). STANC achieves the transmitting diversity (virtual MIMO) and supports big data networks under low transmitting power conditions. Furthermore, this study evaluates the impact of relay location on smart devices network performance in increasing interfering and scattering environments. The performance of STANC is analyzed for Internet of Things (IoT) applications in terms of Symbol Error Rate (SER) and the outage probability that are calculated using analytical derivation of expression for Moment Generating Function (MGF). In addition, the ergodic capacity is analyzed using mean and second moment. These expressions enable effective evaluation of the performance and capacity under different relay location scenario. Different fading models are used to evaluate the effect of multipath scattering and strong signal reflection. Under such unfavourable environments, the performance of STANC outperforms the conventional methods such as physical layer network coding (PNC) and ANC adopted for two way transmission

A close shave: masculinity and bodywork in dementia care

This snapshot focuses on a doctoral study at the University of Manchester which is situated within a larger ESRC funded study, ‘The Hair and Care Project’, which seeks to explore the relationship between appearance and personal identity for people with dementia. The doctoral study accesses data from this wider study (http://www.esrc.ac.uk/my-esrc/grants/RES-061-25-0484/read). The snapshot presents male shaving as a case example of bodywork in dementia care and argues that male shaving could be a revealing lens through which to explore masculinity and embodiment in dementia care