Khaled Shaalan

The advent of technology has changed the way of government to communicate with its stakeholders by incorporating the notion of e-government based on web-portal. These web-portals whereas identified successful in communicating with the stakeholders also played a vital role in sharing and managing knowledge. Based on this notion, this article is concerned with the role of e-government portal in sharing knowledge and the way through which this portal increases the efficiency of entire public sector. In the same instance, the K-ACT model of internet portal is analyzed to identify the features that make a web-portal best and perfect. In this article, we conducted a literature review and focused our attention on analyzing the e-government portals of four countries i.e. China, Hong Kong, Beijing and Turkish Municipalities. For the analysis and comparison purposes, the checklist of K-ACT model is used. The findings revealed that the e-government portal of these countries is not fully matched with the features as described in K-ACT, which ultimately affect the knowledge management and sharing practices in the region. Therefore, recommendations are made to improve the existing portal for knowledge sharing so that citizens can take more knowledge benefits from the e-government portals.

Keyword identification is an important task that provides useful information for NLP applications including: document retrieval, clustering, and categorization, among others. State-of-the-art methods rely on local features of words (e.g. lexical, syntactic, and presentation features) to assess their candidacy as keywords. In this paper, we propose a novel keyword identification method that relies on representation of text abstracts as word graphs. The significance of the proposed method stems from a flexible data representation that expands the context of words to span multiple sentences and thus can enable capturing
of important non-local graph topological features. Specifically, graphlets (small subgraph patterns) were efficiently extracted and scored to reflect the statistical dependency between these graphlet patterns and words labeled as keywords. Experimental results demonstrate the capability of the graphlet patterns in a keyword identification task when applied to MEDLINE, a standard research abstract dataset.

The study tries to find out the most important features in building games based on the grossing. The study is limited to fifty iPhone games that have achieved top grossing in the USA. The game features were extracted from a previous study [1] and classified through ARM funnel into five groups (“A”, “R”, “M”, “AR”, and “RM”). The paper follows CRISP-DM approach under SPSS Modeler through business and data understanding, Data preparation, model building and evaluation. The researcher uses Decision Tree model since the features have closed value i.e. (Yes/No) on the grossing weight. The study reached to the most important 10 features out of 31. These features are important to build successful mobile games. The study emphasizes on the availability of (Acquisition, Retention and Monetization) elements on every successful game and if any is missed, will lead to the failure of the game.

Mobile learning (M-learning) has become an important educational technology component in higher education. M-learning makes it possible for students to learn, collaborate, and share ideas among each other with the aid of internet and technology development. However, M-learning acceptance by learners and educators is critical to the employments of M-learning systems. Attitudes towards M-learning technology is an important factor that helps in determining whether or not learners and educators are ready to use M-learning. Such attitudes will serve to identify strengths and weaknesses and facilitate the development of the technology infrastructure. This paper aims at exploring students and educators' attitudes towards the use of M-learning in higher educational universities within Oman and UAE; two neighboring countries in the Arab Gulf region. To serve this purpose, two survey questionnaires were conducted: one for students and another for educators. The participants of this study are 383 students and 54 instructors from five universities. Different factors have been examined to test where there is a significant difference among students and educators' attitudes towards the use of M-learning, such as gender, age, country, level of study, smartphone ownership, major in terms of students and age, country, academic rank, academic experience and smartphone ownership in terms of educators. Findings revealed significant differences among the students’ attitudes towards M-learning with regard to their smartphone ownership, country and age. Furthermore, results indicated that M-learning can be one of the promising pedagogical technologies to be employed in the higher educational environments within the Arab Gulf countries.