Managing Admission in Saudi Universities: a System Approach

This research aims at the development of a framework for designing a proposed admission system in the Saudi universities. This study is based on results of another paper that revealed that the current admission system in the Saudi universities needs to develop. This due to the current system for admission in Saudi universities is not comprehensive, and not relevant. The study recommends applying modern approaches related to DSS (such as; the proposed system) in order to improve the efficiency of the admission system in the Saudi universities

Energy efficient algorithm for swarmed sensors networks

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this work we are presenting the design of an intelligent hybrid optimization algorithm which is based on Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks (WSNs). It is composed of two phases; Phase-1 is designed to divide the sensor nodes into independent clusters by using Genetic Algorithms (GAs) to minimise the overall communication distance between the sensor-nodes and the sink-point. This will decrease the energy consumption for the entire network. Phase-2 which is based on Particle Swarm Optimization (PSO) is designed to keep the optimum distribution of sensors while the mobile sensor network is directed as a swarm to achieve a given goal. One of the main strengths in the presented algorithm is that the number of clusters within the sensor network is not predefined, this gives more flexibility for the nodes’ deployment in the sensor network. Another strength is that sensors’ density is not necessary to be uniformly distributed among the clusters, since in some applications constraints, the sensors need to be deployed in different densities depending on the nature of the application domain. Although traditionally Wireless Sensor Network have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs

Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification

Class discovery from semi-structured EEG data for affective computing and personalisation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not explore the inter-relationships between the data collected missing out on any correlations that could tell us interesting facts beyond emotional recognition. This second issue would be of particular interest to psychologists and medical professions. In this paper, we investigate the use of Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be translated into classes. We start by training varying sizes of SOM with the EEG data provided in a public dataset (DEAP). The produced graphs showing Neighbour Distance, Sample Hits, Weight Position are analysed holistically to identify patterns in the structure. Following that, we have considered the ground- truth label provided in DEAP, in order to identify correlations between the label and the clustering produced by the SOM. The results show the potential of SOM for class discovery in this particular context. We conclude with a discussion on the implications of this work and the difficulties in evaluating the outcome

Improving Security in Bring Your Own Device (BYOD) Environment by Controlling Access

With the rapid increase in smartphones and tablets, Bring Your Own Devices (BYOD) has simplified computing by introducing the use of personally owned devices. These devices can be utilised in accessing business enterprise contents and networks. The effectiveness of BYOD offers several business benefits like employee job satisfaction, increased job efficiency and flexibility. However, allowing employees to bring their own devices could lead to a plethora of security issues; like data theft, unauthorised access and data leakage. This paper investigates the current security approaches and how organisations can leverage on these techniques regarding policies, risks and existing security techniques to mitigate or halt the security challenges. This research aimed to fill up the access control gap in the BYOD environment by developing an Intelligent Filtering Technique (IFT) using Artificial Intelligence (AI) Technique. Based on the behavioural patterns of device packets Inter-Arrival-Time (IAT) features through network traffic flow packet headers (Such as Transmission Control Protocol (TCP), User Datagram Protocol (UDP) and Internet Control Messaging Protocol (ICMP))

Digital Futures Symposium

Call for papers We invite contributions to a Special Issue on Big Data, AI and Digital Futures: Challenges, changes and continuities, to be published by the AI & Society Journal of Culture, Knowledge and Communication (Springer) http://link.springer.com/journal/146. This special issue arises from the Big Data, AI and Robotics (BDAIR 18) Research Symposium at the De Montfort University (DMU). The main objective of this special issue is to encourage cross-disciplinary, interdisciplinary research and international research collaboration. ============================= SPECIAL ISSUE THEMES ============================= Aims: The aim of this special issue is to address the societal complex issues emerging from the recent advances in AI. As we look for answers to address new challenges, we look at the impact of AI and big data on our futures in the context of society. Content How might algorithms and big data shape our digital futures? In what ways can the semantic web impact our everyday life? Are there ways of envisioning a structure for managing data in a meaningful way, which may offer a transformational experience? We are witnessing a shift in political, social, cultural and technical relations which are increasingly driven by big data and algorithms. Our external environment is being codified leading to an increased level of surveillance both at personal and professional levels. This in itself is a challenge to privacy and data protection. We are already experiencing self-monitoring and tracking with the devices we wear that prompt us to engage in certain behaviours. Are we far from a day when technology will induce behavioural changes, not only at cognitive level but also at conative levels? What for claims that Big Data will make theory redundant? What ontological and epistemological issues arise in relation to these technologies? Our thoughts, emotions and actions are increasingly getting interpellated by algorithms and data. How does that then impact on the ‘Logos-Pathos-Ethos’ of our lives? Sophia bot froze on the question of corruption in Ukraine. On the other hand, we witnessed “the great British Brexit robbery” (Guardian, 2017) that proved whoever owns the data actually wins the campaign, election and the world. Cambridge Analytics Brexit has been one of the popular searches on the internet. At the same time, big data pose challenges as they generate noise and that means data often can be indecipherable, bewildering and recherché. Disruptions are common when we deal with data in any subject area. Therefore, it is cardinal to address the technological complexity, not only through academic research, scholarship and pedagogic practice but also industry engagement. On the other hand, big data and algorithms embed innovation and we encounter technologies in a transformational way, where conversations and dialogic interventions are rapid. Perhaps due to the contrasting ways in which we engage with big data and algorithms, the need for well-defined theoretical frameworks and methodological tools are increasingly in demand Siapera, 2018). Readership National and International We will invite experts both nationally and internationally to contribute to this special issue Goal Our goal is to offer an interdisciplinary coverage of the area explored, by bringing together perspectives from different domains such as computer science, design studies, business, cultural anthropology, arts and humanities and social sciences. In particular, we welcome contributions that explore the following themes: Themes Topics include, but are not limited to, the following: Media datafication and neoliberalism Data and business Social media and big data Big data, PR and Advertising Big data and politics Ethics, privacy and technology Data and sustainability Personalisation, Machine learning and AI Social bots and the management of sociality Quantified self and data cultures Data and education Researching media and culture using data methods Data visualisation, art and design Social responsibility and innovation Data and health Mobile and locative media Data and surveillance Using Big Data to test social theories Social data collection and novelt

The effects of typing demand on learner's Motivation/Attitude-driven Behaviour (MADB) model with mouse and keystroke behaviours

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.It would be desirable to have an automated means of assessing a learner's motivation and stress levels in an e-learning system, which would give impact on his or her learning performance. This preliminary research examines the effects of typing task demand on Motivation/Attitude-driven Behavior (MADB) model. The model is adapted from what was proposed by Wang [1], which is used to describe how the motivation process drives human behaviours and actions, and how the attitude and decision-making process help to regulate and determine the action to be taken by the learner. The effects of typing demand are tested on learners' stress perceptions, motivation, attitudes, decision, as well as their mouse and keystroke behaviours. The typing demand is varied by the pre-defined text length and language familiarity. The results of Multivariate Analysis of Variance and correlation tests are generally congruent with the MADB model proposed by Wang, but with minor difference. We also found that a learner's behaviour is significantly correlated to his or her mouse and keystroke behaviours. A revised version of MADB model based on e-learning environment is proposed

Designing Learning Management System to Encourage ELearning Sustainability

Many universities have been employing Learning Management System (LMS) in their educational programs for many years. However, sustaining the e-learning environment has become a great challenge for these institutes. Although there was much research conducted to study the success factors of a LMS, understanding the impact of user interface, navigation and usability designs, which may affect the user experience in virtual learning environment, is equally important. It is suggested that during the design stage the instructor should plan and structure the resources to assure interactions that assist in the transfer of skills and knowledge. In addition we can use tools such as email, chat rooms, and discussion boards to provide learners the opportunities to interact and add a new level of depth into their learning. It is also necessary to conduct a complete series of evaluations for testing the accuracy, effectiveness and clarity of the e-learning system. Therefore this research aims to evaluate the effectiveness and clarity of LMS design to encourage e-learning sustainability. We investigate the effectiveness of the LMS in assisting knowledge transfer and interactivity in the virtual learning environment, based on three areas: navigation design, user interface design and usability of the discussion board. An online questionnaire survey was conducted to collect data from students and instructors regarding their experiences with the LMS, and their satisfaction levels in these three areas, as well as to suggest areas of improvements

An Outcome based approach to developing a Belarusian Qualification Framework

http://iesed.esy.es/The Higher Education landscape of Belarus is characterised by high quality institutions offering world class expertise and facilities, and a very high participation rate in higher education. However, it has also been recognised by the state that the degree of individuality and autonomy prevalent in these institutions works against the current mood of globalisation in Higher Education. An obvious example is international exchanges. It is particularly difficult in the case of students since the programmes are usually organised in an insular way and lack a precise specification of the level at which any contributory course is delivered. A stated objective of the Belarusian Ministry of Education is to seek membership of the European Higher Education Area (EHEA). To this end a road map (Eastern Partnership Civil Society Forum, 2017), designed to afford increased international compatibility of the Belarusian Higher Education Framework, has been defined and is being implemented by the Belarusian Ministry of Education. This paper considers how the EU funded project IESED could directly contribute to the realisation of this Road Map