An alternative model of intercultural sensitivity replicated from Chen and Starosta’s five-factor model of Intercultural sensitivity was replicated validated at eight universities in Taiwan and Hainan, China. Both Confirmatory factor analysis and exploratory factor analysis were conducted to inspect the construct validity of the alternative model of Intercultural Sensitivity. Results indicated that three models fitted the data and the values of indicators were deemed acceptable; however, three-factor model of intercultural sensitivity with 10 items significantly fitted the data better than Chen and Starosta’s five-factor model, Wu’s four-factor model, and the revised two-factor model. This three-factor model, an alternative model, consists of three factors based on 10 items from the 24-item Intercultural Sensitivity Scale by Chen and Starosta; the reliability coefficient was .771, demonstrating high internal consistency. Participants from both Taiwan and Hainan represent ethnic Chinese who, although sharing similar cultures, still present cultural differences. This present study suggests an alternative model of the Intercultural Sensitivity that fits the characteristics of a sample of undergraduates from Hainan, China and Taiwan by reproducing Chen and Starosta’s ISS.

Knowledge sharing has become an important tool in every organization’s success story today. Various knowledge management applications has been carried out recently in many sector like health care, university, banking, construction etc. and it has brought significant improvements. However minimal research is done on career development and as such Knowledge management in career exploration in Engineering Education has not been carried out till date. In India, most of the curriculum and pedagogy does not suit with the real time job environment and this caused many graduates to remain unemployed every year. To meet the gap, this paper attempts to highlight the works on the various existing career exploration and knowledge management techniques. The author collected relevant literature from 2010 to 2015 with the objective to find out a way to combine the two disciplines of career exploration and knowledge management to enhance the career awareness and career exploration system with the application of knowledge management techniques. From the study, the researcher found four conclusions: (i). Education and work/career are separated into different world; (ii). Career exploration enhances student’s career awareness; (iii). Knowledge management technique adds efficiency and enhances curriculum; (iv). The research efforts in knowledge management (KM) and career exploration are sparse and no such literatures are available on knowledge management in career exploration currently. This research may add to the development of an efficient knowledge management based career exploration system for engineering education.

The purpose of the present research is to designate the effects of Scratch-based game activities on students’ attitudes towards learning computer programming, self-efficacy beliefs and levels of academic achievement. The research was conducted through a pre-test – post-test control group quasi-experimental study. The study group consists of 49 students studying at the Faculty of Engineering. The test group was administered a teaching method with Scratch-based game activities. On the other hand, the control group was directly taught C++ topics via an editor. Research data were collected via an implementing academic achievement test (Kr-20= 0, 71), attitude towards a learning programming scale (Cronbach’s Alpha =0.84) and a computer programming self-efficacy scale (Cronbach’s Alpha= 0.966). Our findings are as follows: A significant number of the students consider themselves as mid-level efficient in C++ programming. Scratch-based game activities render no effects on students’ attitudes and self-efficacy perceptions. On the other hand, Scratch-based game activities render significant contributions on students’ academic achievements in C++ programming language.

The image segmentation performs a significant role in the field of image processing because of its wide range of applications in the agricultural fields to identify plants diseases by classifying the different diseases. Classification is a technique to classify the plants diseases on different morphological characteristics. Different classifiers are used to classify such as SVM (Support Vector Machine), K- nearest neighbor classifiers, Artificial Neural Networks, Fuzzy Logic, etc. This paper presents different image processing techniques used for the early detection of different Plants diseases by different authors with different techniques. The main focus of our work is on the critical analysis of different plants disease segmentation techniques. The strengths and limitations of different techniques are discussed in the comparative evaluation of current classification techniques. This study also presents several areas of future research in the domain of plants disease segmentation. Our focus is to analyze the best classification techniques and then fuse certain best techniques to overcome the flaws of different techniques, in the future.

Vehicular ad hoc networks (VANETs) are the networks, which configured themselves, where the nodes are moving vehicles. These provide the communications required to deploy Intelligent Transportation Systems (ITS). A major dispute in VANETs is distribution of efficient and computable information because during communication nodes may leave or join the network dynamically. There is no guarantee about node availability at any given time, which leads to traffic problem, congestion problem. Therefore trailing the favorable path is a challengeable issue. Multiple routing algorithms have been developed for routing solution. In this paper the swarm-based algorithm has been presented, which helps to find out the optimal route using Bellman Ford algorithm. Ant colonization searches out the path using pheromones level. Higher the pheromone count of a route gives the optimal choice of path that can be used for packet delivery. Bellman Ford Algorithm optimizes the paths found by Ant Colony Optimization (ACO) by comparing the distance of source to all the nodes of network or cost given to the networks.

With the outgrowth of cloud computing, a large amount of private information is stored over cloud servers, which is in encrypted format. But searching over encrypted data is very difficult. Earlier search schemes were based on Boolean search through keywords. But don’t consider relevance of files. After that ranked search comes into its role, which uses searchable symmetric encryption (SSE). To achieve more practical and efficient design method was further modified to “Order preserving symmetric encryption” (OPSE), which uses primitives and indexed metadata files used in ranked SSE. In this proposed work further enhancements are done to reduce storage space for encrypted metadata using Porter Stemming method. Improvements in retrieval time are also done by using Boyer Moore’s searching algorithm.

This research aims to find the reasons which affects students in college of applied sciences in Ibri. The programming courses are very important for the Information technology students in general.
The sample consisted of 50 students from Ibri College. A questionnaire was developed for the purpose of this study. Content validity and reliability were ensured to be sufficient for the study.
The finding of study showed that the students faced some problems during their study in the programming courses like the short time for practical, some of them as their English language is not good so it also affects their skills and knowledge in programming.
Finally this study presents recommendations and advice to improve student’s skills and help them to avoid getting low marks in programming courses.

According to the survey of World Health Organization (WHO), the number of people getting affected by glaucoma eye disease in worldwide will be 79.11 million by the year 2020. Glaucoma is a dangerous eye disease, which can lead to permanent vision loss if not provided proper treatment at the right time. Currently ophthalmologists detect the glaucoma disease based on estimation of cup to disk ratio, but this method suffers from accurate segmentation of regions like optic disk and optic cup. However, this introduces errors in the diagnosis. Therefore in this paper, Hausdrop Fractal Dimension (HFD) technique is adopted for identification of the glaucoma eye disease. Here, Optic disk perimeter parameter is used in HFD technique for classification of healthy or glaucomatous retinas. Average fractal dimension is calculated for a set of healthy optic disks and the fractal dimension is found to be 0.998, whereas for glaucomatous optic disks obtained average fractal dimension value 1.342.