http://www.seipub.org/mt/RSS.aspxen-US2015-0<p class="abstract">Interaction Triangle of Mobile Learning & E-Learning and Computer Tools (CUAELML) in the Basic Class: Attitudes & Opinions of Pre-Service Teachers</p><ul><li>Pages 1-11</li><li>Author Gamal Ahmed Ahmed Abdullah AlawiMohammed ShwalNakhat Nasree</li><li>Abstract Computers, m-learning and e-learning which are the most significant tools of information age, have increasingly been used in each stage of education system. The participants were two hundreds and fifteen (215) pre-service teachers who have participated in this study. Specific influences between m-learning, e-learning and computer tools are presented. The implications for CUAELML and suggestions for pre-service teachers’ opinions and attitudes are discussed. According to analysis results, the attitude of the pre-service teachers regarding e-learning has the higher positive effect on m-learning rather than computer usage. A model, which explained the effect of AEL, ML and CU on learning, was established and tested. Using AMOS 18 (Analysis of Moment Structures) program, it explained 50% of CU TOOLS, 64% of ML TECHNOLOGY and 67% of AEL, with good model fit.</li></ul>http://www.seipub.org/mt/PaperInfo.aspx?ID=15761Multimedia Technology http://www.seipub.org/mt/PaperInfo.aspx?ID=157612015-0<p class="abstract">Local Entropy Descriptor of Motion History Image for Human Action Recognition</p><ul><li>Pages 12-20</li><li>Author De Zhan</li><li>Abstract Human action recognition is one of the most promising research areas at the moment. In this paper, we present an entropy based effective approach to recognize human activities from video sequences. Our approach provides a new description directly from Motion History Image (MHI) which is an efficient real-time representation for human action. Local entropy of pixels in MHI is computed to characterize the texture of motion filed. It helps to match the moment-based feature statistically. In an evaluation of this on well established benchmark data sets, we achieve high recognition rates. Using the popular Weizmann data set, we achieve the best accuracy of 98.9% in a leave-one-out cross validation procedure. Using the well-known KTH data set, we evaluate the robustness of the proposed approach over different scenarios. As a result, this new method is computationally efficient, robust with respect to appearance variation, and straight forward to be applied as it builds itself on well established and understandable concepts.</li></ul>http://www.seipub.org/mt/PaperInfo.aspx?ID=24529Multimedia Technology http://www.seipub.org/mt/PaperInfo.aspx?ID=24529