Third Revolution Digital Technology in Disaster Early Warning

Networking societies with electronic based technologies can change social morphology, where key social structures and activities are organized around electronically processed information networks. The application of information and communications technologies (ICT) has been shown to have a positive impact across the emergency or disaster lifecycle. For example, utility of mobile, internet and social network technology, commercial and amateur radio networks, television and video networks and open access technologies for processing data and distributing information can be highlighted. Early warning is the key function during an emergency. Early warning system is an interrelated set of hazard warning, risk assessment, communication and preparedness activities that enable individuals, communities, businesses and others to take timely action to reduce their risks. Third revolution digital technology with semantic features such as standard protocols can facilitate standard data exchange therefore proactive decision making. As a result, people belong to any given hierarchy can access the information simultaneously and make decisions on their own challenging the traditional power relations. Within this context, this paper attempts to explore the use of third revolution digital technology for improving early warning

Resource-constrained classification using a cascade of neural network layers

Deep neural networks are the state of the art technique for a wide variety of classification problems. Although deeper networks are able to make more accurate classifications, the value brought by an additional hidden layer diminishes rapidly. Even shallow networks are able to achieve relatively good results on various classification problems. Only for a small subset of the samples do the deeper layers make a significant difference. We describe an architecture in which only the samples that can not be classified with a sufficient confidence by a shallow network have to be processed by the deeper layers. Instead of training a network with one output layer at the end of the network, we train several output layers, one for each hidden layer. When an output layer is sufficiently confident in this result, we stop propagating at this layer and the deeper layers need not be evaluated. The choice of a threshold confidence value allows us to trade-off accuracy and speed. Applied in the Internet-of-things (IoT) context, this approach makes it possible to distribute the layers of a neural network between low powered devices and powerful servers in the cloud. We only need the remote layers when the local layers are unable to make an accurate classification. Such an architecture adds the intelligence of a deep neural network to resource constrained devices such as sensor nodes and various IoT devices. We evaluated our approach on the MNIST and CIFAR10 datasets. On the MNIST dataset, we retain the same accuracy at half the computational cost. On the more difficult CIFAR10 dataset we were able to obtain a relative speed-up of 33% at an marginal increase in error rate from 15.3% to 15.8%

Building Networks of Practice

{Excerpt} Extensive media coverage of applications such as FaceBook, MySpace, and LinkedIn suggests that networks are a new phenomenon. They are not: the first network was born the day people decided to create organizational structures to serve common interests—that is, at the dawn of mankind. However, the last 10–20 years have witnessed rapid intensification and evolution of networking activities, driven of course by information and communication technologies as well as globalization. These make it possible for individuals to exchange data, information, and knowledge; work collaboratively; and share their views much more quickly and widely than ever before. Thus, less and less of an organization’s knowledge resides within its formal boundaries or communities of practice. Knowledge cannot be separated from the networks that create, use, and transform it. In parallel, networks now play significant roles in how individuals, groups, organizations, and related systems operate. They will be even more important tomorrow. Since we can no longer assume that closely knit groups are the building blocks of human activity—or treat these as discrete units of analysis—we need to recognize and interface with less-bounded organizations, from non-local communities to links among websites. We should make certain that knowledge harvested in the external environment is integrated with what exists within, especially in dynamic fields where innovation stems from inter-organizational knowledge sharing and learning. Therefore, the structure and composition of nodes and ties, and how these affect norms and determine usefulness, must become key concerns. This makes the study of networks of practice a prime interest for both researchers and practitioners

A Hybrid Approach to Network Robustness Optimization using Edge Rewiring and Edge Addition

Networks are ubiquitous in the modern world. From computer and telecommunication networks to road networks and power grids, networks make up many crucial pieces of infrastructure that we interact with on a daily basis. These networks can be subjected to damage from many different sources, both random and targeted. If one of these networks receives too much damage, it may be rendered inoperable, which can have disastrous consequences. For this reason, it is in the best interests of those responsible for these networks to ensure that they are highly robust to failure. Since it is not usually feasible to rebuild most existing networks from scratch to make them more resilient, it is necessary to have an approach that can modify an existing network to make it more robust to failure. Previous work has established several methods of accomplishing this task, including edge rewiring and edge addition. Both of these methods can be very useful for optimizing network robustness, but each comes with its own set of limitations. This thesis proposes a new hybrid approach to network robustness optimization that combines both of these approaches. Four edge rewiring based metaheuristic approaches were modified to incorporate one of three different edge addition strategies. A comparative study was performed on these new hybrid optimizers, comparing them to each other and to the vanilla edge rewiring only approach on both synthetic and real world networks. Experiments showed that this new hybrid approach to network robustness optimization leads to much more highly robust networks than an edge rewiring only approach

The Virtual Library : changing roles and ethical challenges for librarians

In this age of Information Technology, there have been so many opportunities for the Librarians for involvement in an information-based society including electronic and multimedia publishing, Internet based-information services, global networking, web based digital resources etc. The base of recorded information is growing at an accelerating rate, in increasing varieties of formats (texts, numeric, graphic, video, audio, image, electronic, etc.). Virtual Library can be defined as the Internet based Digital Library. The concept of “Virtual Library” is that any person who has a computer by which he can make connection to the library networks can access not only the resources of the library but also access variety of information that is available nationally and internationally through networks, like Internet, Intranet without being physically present in library. This article explains what is meant by a Virtual Library and to build a virtual library in what way the Librarians have to change their roles to face the challenges of modern Information Technologies in the changing environment. Also this article, discusses about the changing role of librarians, the skills needed to build a virtual library and how the virtual information should be collected, managed, preserved and disseminated to the millennium users

Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table