Mobile augmented reality is growing rapidly because of the growth of smartphones. Due to the portable

nature of smartphones, mobile augmented reality devices have become the most widely deployed consumer

augmented reality display device and show promise for becoming the first commercial success for augmented

reality technologies. The role of the user is identified and forwarded to the respective module. Cognitive

engagement and interactivity are the main two factors of influenced learning where the engagements are focused in

Learnability identification module. When a common, single optical tracking platform is available the reachability

and performance can also be increased without affecting the efficiency, which will be done in AllinoneAR Module.

An integrated framework consisting of three above stated modules is proposed in this paper

l security perspective of Cloud computing with the aim to highlight the problem from the cloud stakeholders' perspective, and the cloud service delivery models perspective. The paper aims also to introduce the mechanisms used to secure cloud computing applications as well as to compare some providers of cloud computing field with each other in general and security aspects. 

The continuous call on the increase of the quality of teaching put more pressure on academics to

draw on students experiences and propose new approaches for delivering information and increasing the

standards of teaching excellence. There are several generalized approaches to the scholarship of teaching and

learning such as continuous professional development and learning by developing, however cooperative

learning combined with project-based learning can be used in a range of disciplines in graduate studies such

as Information Technology. From our experience in teaching: e-commerce subject and web application

security course we found that such a strategy encourages lecturer to continue improve the curriculum and

delivery process leading to better learning experience for the students. This paper reports on the practical

nature of cooperative learning and how to use it to bridge Teaching and Learning as well as Theory and

Application. Our purpose is to report on cooperative learning in graduate studies using Project-based learning

strategy. It demonstrates the value of student empowerment and leadership in autonomous project groups.

The strategy is designed primarily to increase student engagement and improve the learning process. We

evaluate the success of the strategy by evaluating student attendance and active participation in classroom

discussions, learning outcomes, and student results. The success of the strategy encourages us to incorporate

the project-based approach on more than one course

Compiler writers have developed various techniques, such as constant folding, subexpression elimination, loop transformation and vectorization, to help compilers in code optimization for performance improvement. Yet, they have been far less successful in developing techniques or cost models that compilers can rely on to simplify parallel programming and tune the performance of parallel applications automatically. This paper is the first of two-phase study to develop an analytical model that can be used to estimate the cost for sequential and parallel execution of array expressions on multicore architectures. While this paper discuss the possibility of developing a cost model to estimate the sequential execution of array expressions on a single CPU, the second part of the investigation shall focus on developing a model to estimate parallel execution of arrays on multicore platforms. The model presented in this paper is expected to be used by programming language compilers as complement component with the other model to estimate and subsequently decide whether to parallelize individual array expressions or not. The preliminary results which are presented here show that this model can give a satisfactory evaluation and high-precision estimation for the cost of executing a regular array expression on a single core processor.

Genetic algorithms (GAs) are based on techniques inspired by some aspects of natural science such as inheritance, reproduction and mutation, and they are used as optimization technique for searching large solution spaces. In computer science, for example, they could be used in data sorting and searching, circuit design and to improve application performance the quality of designed tools such as code generation. This paper looks at the possibility of using genetic algorithms to ameliorate the automatic construction of code generators. Experimental evidence is provided that the use of such algorithms can improve the quality of automatically constructed code generators.

neural network is considered as a nonlinear dynamic system consisting of a large number of simple processing elements interconnected in some manner with adjustable weighted strength. Neural networks provide qualitative and quantitative (analog, digital or logical) knowledge through information coding and decoding, and have powerful functions in learning and selforganization. These properties make neural networks considered to be more powerful in dealing with numerical data than other artificial intelligent systems like expert systems. However, the performance of neural networks depends deeply in number of factors including transfer function, number of hidden layers, number of nodes in hidden layers, input function, and weight function. In this article we present a comparative study of these factors and how they influence the performance of a system. 

This paper presents empirical methods for enhancing the accuracy of inductive learning systems. It addresses the problems of: learning propositional production rules in multi-class classification tasks in noisy domains, maintaining continuous learning when confronted with new situation after the initial learning phase is completed, and classifying an object when no rule is satisfied for it.

It is shown that interleaving the learning and performance-evaluation process allows accurate classifications to be made on real-world data sets. The paper presents the system ARIS which implements this approach, and it is shown that the resulting classifications are often more accurate than those made by the non-refined knowledge bases.

The core design decision that lies behind ARIS is that it employs an ordering of the rules according to their weight. A rule’s weight is learned by using Bayes’ theorem to calculate weights for the rule’s conditions and combining them. This model focuses the analyses of the knowledge base and assists the refinement process significantly.

The system is non-interactive, it relies on heuristics to focus the refinement on those experiments that appear to be most consistent with the refinement data set. The design framework of ARIS consists of tabular model for expressing rule weights, and the relationship between refinement cases and the rules satisfied for each case to focus the refinement process. The system has been used to refine knowledge bases created by ARIS itself, as well as to refine knowledge bases created by the RIPPER and C4.5 systems in ten selected domains.