Current Issue : April - June Volume : 2016 Issue Number : 2 Articles : 4 Articles
Augmented reality (AR) has the potential to create compelling learning experiences.\nHowever, there are few research works exploring the design and evaluation of AR for\neducational settings. In our research, we treat AR as a type of multimedia that is\nsituated in authentic environments and apply multimedia learning theory as a\nframework for developing our educational applications. We share our experiences in\ndeveloping a handheld AR system and one specific use case, namely, situated\nvocabulary learning. Results of our evaluations show that we are able to create AR\napplications with good system usability. More importantly, our preliminary\nevaluations show that AR may lead to better retention of words and improve\nstudent attention and satisfaction....
These days, the advancing of smart devices (e.g. smart phones, tablets, PC, etc.) capabilities and the increase of internet bandwidth\nenables IPTV service provider to extend their services to smart mobile devices. User can just receive their IPTV service using any\nsmart devices by accessing the internet via wireless network from anywhere anytime in the world which is convenience for users.\nHowever, wireless network communication has well a known critical security threats and vulnerabilities to user smart devices\nand IPTV service such as user identity theft, reply attack, MIM attack, and so forth. A secure authentication for user devices and\nmultimedia protection mechanism is necessary to protect both user devices and IPTV services. As result, we proposed framework\nof IPTV service based on secure authentication mechanism and lightweight content encryption method for screen-migration in\nCloud computing. We used cryptographic nonce combined with user ID and password to authenticate user device in any mobile\nterminal they passes by. In addition we used Lightweight content encryption to protect and reduce the content decode overload\nat mobile terminals. Our proposed authentication mechanism reduces the computational processing by 30% comparing to other\nauthentication mechanism and our lightweight content encryption reduces encryption delay to 0.259 second....
The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications\nto provide services with the expected quality for their users.However, factors like the network parameters and codification can\naffect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity\nto evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs\n(Back propagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics\nMOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video\nQuality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service)\nbased in the strategy Diffserv.The metrics were analyzed through Pearson�s and Spearman�s correlation coefficients, RMSE (Root\nMean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics....
A novel systemof image retrieval, based onHadoop and Spark, is presented.Managing and extracting information from Big Data is\na challenging and fundamental task. For these reasons, the system is scalable and it is designed to be able to manage small collections\nof images as well as huge collections of images. Hadoop and Spark are based on the Map Reduce framework, but they have different\ncharacteristics. The proposed system is designed to take advantage of these two technologies. The performances of the proposed\nsystem are evaluated and analysed in terms of computational cost in order to understand in which context it could be successfully\nused.The experimental results show that the proposed system is efficient for both small and huge collections...
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