Nowadays, Data Centre Network (DCN) architectures consist thousands of resources such as servers, routers, switches etc. These devices are interconnected using communication network. Data Centers are growing dynamically and the fundamental challenge is on how to interconnect the devices within it. The growth of Data centers implies to these physically connected devices as well as to the robust application services it supports. Efficient data center network topologies need to handle the demands requested as a result of the growth. Data center network topologies have to address the basic design issues. The traditional network topology isn’t cost-effective to handle the demands of the growing data centers. Even though it deploys expensive devices, it couldn’t satisfy the needs of data centers. Recently some network topologies that deploy commodity devices are introduced as an alternative solution. This paper focuses on three approaches of topologies that deploy commodity devices. These are Fat Tree, Dcell and Jellyfish. These topologies give better performance compared to the tree based hierarchical topologies.
Online video search or stream live on social media have become tremendous widespread and speedy increased continuously in recent years. Most of the videos shared on social media are aimed at the more number of views from audiences. What and how many videos the users shared all around the world have created a great amount and varied videos and the other data into Internet cloud’s database and even can be viewed as a kind of big data of digital contents. This research is to present how to implement a social-driven tags computing (SDT) which can be used to facilitate online video search on social media platforms.
The usability testing of mobile applications involving persons with Down syndrome is an issue that has not be comprehensively investigated and there is no single proposal that takes on board all the issues that could be taken into account. This study aims to propose a practical guide ¨USATESTDOWN¨ to measure and evaluate the usability of mobile applications focusing on Down syndrome users and their primary limitations. The study starts with an analysis of existing methodologies and tools to evaluate usability and integrates concepts related to inspection and inquiry methods into a proposal. The proposal includes the opinions of experts and representative users; their limitations, the applicability during the development process and the accessibility. This guide is based on the literature review and the author’s experience in several workshops where persons with Down syndrome used mobile devices.
The rapidly changes of service technology in today’s global market require unprecedented levels of interoperability to integrate diverse services to share knowledge and collaborate among organizations. In the real world, services are heterogeneous, they have been developed by different vendors, using different technology and deployed in different environment. The heterogeneity of services brings challenge in the domain of integration. The shift of technology has forced many service enterprises to develop an integration approach that will be proponent to the changes. Prior attempts use adapter models, middleware’s and other integration techniques. However, that has not removed the challenges of heterogeneous service integration. In this paper we contemplate the existing integration approaches and examine the requirements of suitable techniques. We then propose new model that will enable heterogeneous service integration with a minimal user-intervention.
Object recognition is the process of identification of an object in an image. There exist various algorithms for the same. Appearance based algorithms have demonstrated good efficiency, however, their performance gets affected adversely in the presence of clutter or when background changes are affected. We hope to overcome this issue by using Convolution Neural Network (CNN) Theorem. The approach is shape based and has been proven to work well under broad range of circumstances: varied lighting conditions, affine transformations, etc. It involves tiling, which is the phenomenon of the use of multiple layers of neurons to process small portions of the image, which are then used to obtain better representations of the image. This allows CNN to be translation-tolerant. The neural elements learn to recognize objects about which they have no previous information, this ‘learning’ mechanism is affected by the fact that representations of the image are learned by the inner layers of the deep architectures of neurons. Unlike RBM and Autoencoder, which are capable of learning only single global weight matrix layers, the CNN theorem makes use of shared weight in convolution layers, which means that the same filter (weight bank) is used for each pixel in the layer, which reduces the memory footprint and improves performance.