Advance of mobile devices (tablets and smartphones) has enabled everyday users to experience augmented reality. Accordingly, potential usage of augmented reality in education has increased. This paper provides an overview of the current research regarding the use of augmented reality of education and its impact on students. Majority of studies have concluded that usage of augmented reality in classroom has a positive influence on students. Therefore, the main goal of this paper is to show how teachers with basic programming knowledge can build simple, marker-based, mobile augmented reality applications in order to make teaching more interesting to students. First, appropriate development environment is selected, by comparing several different available options. Afterwards, detailed step-by-step process of developing a simple application is given, using the selected Unity 3D environment with Qualcomm Vuforia extension for Unity 3D.
In the current big-data era, business decision making usually involves mining large datasets for finding hidden patterns which can be used for predictions. Such data analytical tasks are far beyond the capabilities of human experts. Artificial Neural Networks (ANNs) are non-linear models that resemble biological neural networks in structure and learn through training. ANNs learn from examples in a way similar to how the human brain learns. Then ANNs take complex and noisy data as input and make educated guesses based on what they have learned from historical data. This paper presents a new learning algorithm for Higher Order Neural Networks (HONNs) which are ANNs in which the net input to a computational neuron is a weighted sum of its inputs plus products of its inputs. The novel learning algorithm is based on Extreme Learning Machine (ELM) algorithm which randomly chooses hidden layer neurons and analytically determines output weights. The experimental results demonstrate that HONN models with the new algorithm offer significant advantages over standard HONN models and traditional ANNs (including Multilayer Perceptrons and RBF Networks), such as faster training and improved generalization abilities.
The research on drug discovery has a long history. The research quality can be improved by using information technology it can be used to model many aspects inside the process. Molecular dynamics is the first stage of the drug discovery process that models the finding of best conformation protein structure. The chosen structure is then used a receptor for several datasets of ligand in virtual screening process. The main goal of this paper is developing an integrated computing platform for drug discovery research using as a service paradigm. Here, the cloud applications is used a bridge between drug discovery tools that running on transparent multiplatform clients. This paper reports the results of the first from three phases of the research -the development of the computing platform interfaces and the analysis of several computing resources including the commercial cloud that will be used by the system.
This paper presents knowledge representation and formalization in different qualitative modeling techniques. Qualitative reasoning, bond graphs, system dynamics, Petri nets and fuzzy cognitive maps are described and compared. They have also been compared with knowledge representation using semantic networks. The conclusion drawn in the paper is that all modeling approaches, observed in the paper, at some phase of a model development use some kind of a cognitive map presented with a directed graph. A cognitive map represents a system by identifying main concepts and relations among concepts them. There are a lot of different cognitive map types representing different kinds of knowledge. Some maps, like fuzzy cognitive maps and causal loops in system dynamics, include knowledge about cause-effect relationships among concepts. Other maps, like envisionment in qualitative reasoning and Petri nets, include knowledge about possible transitions among concepts representing system states variables. Use of cognitive maps in qualitative modeling techniques has been expected because humans use mental cognitive maps to deal with complexity in the surrounding world. That is probably the reason why cognitive maps are widely used in different areas of modeling and simulation to formally represent different type of knowledge.
Data mining has gained popularity in the database f ield recently, the gained knowledge is static becau se of the static nature of the database, and does not ref lect the dynamic nature of knowledge. Extension dat a mining is a product combining Extenics with data mi ning, By using the theory and method of Extenics, i t can mine the knowledge from database which is relative to solve contradictory problems. And the knowledge includes the Extension classification kno wledge, conductive knowledge and other knowledge associated with transformation, which collectively called Extension knowledge. Its Application to enterprise brands segmentation will help the enterp rises to gain their ends on the best way. Research result indicates that extension data mining can provide ef fective decide support for the Decision-making of
