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The article focuses on the social and cultural impact on modern languages due to technology. Topics discussed include relation between virtual and non-virtual world which affects the digital culture, media and globalization, Anglo American influence of modern languages, and other developments include internet poetry, e-fiction and digital cinema.

Purpose Social question and answer (SQA) site is one of the factors that boosted up and popularized the vision of social web. It enables community members to post highly valued answers to globally asked questions and information seekers to grab intellectual information in a contextual, concise, and meaningful format at the cost of investing a few minutes. The purpose of this paper is to present a common architecture, history, and a comprehensive review of such sites.Design/methodology/approach A critical and analytical investigation of the state-of-the-art SQA sites and relevant literature has been carried out with the intention to explore the noticeable features of such sites.Findings By studying relevant literature, and analysing a number of existing systems, a number of research challenges are identified and a generic architecture of SQA sites is contributed.Practical implications The review contributes a comprehensive knowledge about SQA systems and aims to be helpful to new researchers who want to get a broad picture of SQA systems on a single platform. The domain is in its infancy and requires tremendous efforts from the research community to explore its salient aspects with respect to the human world.Originality/value The study inspects SQA sites on a large scale and makes an original contribution by presenting a comprehensive review, future research challenges, and a generic architecture of SQA sites. [ABSTRACT FROM AUTHOR]

Cloud computing emerged as a utility oriented computing that facilitates resource sharing under pay-asyou-go model. Nowadays, cloud offerings are not limited to range of services and anything can be shared as a service through the Internet. In this work, a detailed literature survey with respect to cloud service discovery and composition has been accounted. A proposed architecture with the inclusion of cloud broker is presented in our work. It focuses the importance of suitable service selection and its ranking towards fulfilling the customer's service requirements. The proposed cloud broker advocates techniques such as reasoning and decision making capabilities for the improved cloud service selection and composition. [ABSTRACT FROM AUTHOR]

Present study is targeted towards analysis of the productivity of 'Cloud Computing' literature during 2009-2017. By the analysis of Cloud Computing Literature the highest document credits by Institute therefore V I T University (VITU) ranks first with 20 documents to its credit and School of Information Technology and Engineering (SITE) ranks on Third with 6 documents. Ranking of Journals indicates that highest documents published in International Journal of Advanced Research in Computer Science i.e. 85 documents on Cloud Computing, followed by Indian Journal of Science and Technology published 64 documents & Indian Journal of Computer Science and Engineering published 6 documents on rank 10th. The study also discussed Cloud Computing literature chronologically, Types of documents, most cited articles on cloud computing. [ABSTRACT FROM AUTHOR]

The paper examines 21397 global publications in cloud computing research, ascovered in Scopus database during 2004-13. The annual average growth rate is 96.77%. The global cloud computing research output comes from several countries, of which the top 15 most productive accounts for 90.07% share of the global output. The largest share of output (24.12% and 21.96%) came from China and USA, followed by Germany, India, UK, Taiwan, Australia, Italy, Japan, etc. Austria registered highest share (49.83%) of international collaborative papers among the top 15 countries in cloud computing during 2004-13, Computer science contributed the largest share (79.06%) during 2004-13, followed by engineering (21.20%), mathematics (15.47%), social sciences (4.79%), etc. The top 25 organizations and 20 authors contributed 16.16% and 3.87% share to the global output during 2004-13. Conference proceedings (62.92%) and journals (22.05%) contributed the largest share to global output during 2004-13. The top 20 journals contributed 21.81% share to its total journal output during 2004-13. Only 71 publications (out of total global publications) have registered 100 or more citations since their publication till June 2014 registering an citation impact per paper of 216.68 during 2004-13. [ABSTRACT FROM AUTHOR]

Abstract: Feature selection is an important preprocessing step for many high-dimensional regression problems. One of the most common strategies is to select a relevant feature subset based on the mutual information criterion. However, no connection has been established yet between the use of mutual information and a regression error criterion in the machine learning literature. This is obviously an important lack, since minimising such a criterion is eventually the objective one is interested in. This paper demonstrates that under some reasonable assumptions, features selected with the mutual information criterion are the ones minimising the mean squared error and the mean absolute error. On the contrary, it is also shown that the mutual information criterion can fail in selecting optimal features in some situations that we characterise. The theoretical developments presented in this work are expected to lead in practice to a critical and efficient use of the mutual information for feature selection. [Copyright &y& Elsevier]

In recent years, the problem of learning a real-valued function that induces a ranking over an instance space has gained importance in machine learning literature. Here, we propose a supervised algorithm that learns a ranking function, called ranking instances by maximizing the area under the ROC curve (RIMARC). Since the area under the ROC curve (AUC) is a widely accepted performance measure for evaluating the quality of ranking, the algorithm aims to maximize the AUC value directly. For a single categorical feature, we show the necessary and sufficient condition that any ranking function must satisfy to achieve the maximum AUC. We also sketch a method to discretize a continuous feature in a way to reach the maximum AUC as well. RIMARC uses a heuristic to extend this maximization to all features of a data set. The ranking function learned by the RIMARC algorithm is in a human-readable form; therefore, it provides valuable information to domain experts for decision making. Performance of RIMARC is evaluated on many real-life data sets by using different state-of-the-art algorithms. Evaluations of the AUC metric show that RIMARC achieves significantly better performance compared to other similar methods. [ABSTRACT FROM AUTHOR]

Abstract: The literature is filled with structural optimization articles which claim to minimize costs but which disregard the costs of failure. Due to uncertainties, minimum cost can only be achieved by considering expected consequences of failure. This article discusses challenges in solving real structural optimization problems, taking into account expected consequences of failure. The solution developed herein combines non-linear FE analysis (by positional FEM), structural reliability analysis, Artificial Neural Networks (used as surrogates for objective function) and a hybrid Particle Swarm Optimization algorithm, which efficiently solves for the global optimum. Optimization of a steel-frame transmission line tower is the application example. [Copyright &y& Elsevier]

SUMMARY There are many software pattern catalogs that exist in the published literature today, but none of them uses a consistent classification technique that results in evolving and extensible pattern catalog. We have created a new classification scheme and used it as the basis for constructing a software pattern catalog. It is a uniform approach for creating, maintaining, and expanding the catalog. We present a brief overview of the classification scheme, describe the resulting catalog and the principles underlying its construction, show how to use the catalog, and demonstrate some of its benefits for software designers. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Target detection and classification using unattended ground sensors (UGS) has been addressed in literature. Various tech- niques have been proposed for target detection, but target classification is a challenging task to accomplish using the limited processing power on each sensor module. The major hindrance in using these sensors reliably is, that, the sensor observations are significantly affected by external conditions, which are referred to as context. When the context is slowly time-varying (e.g., day-night cycling and seasonal variations) the usage of the same classifier may not be a good way to perform target classification. In this paper, a new framework is proposed as a Dynamic Data Driven Application System (DDDAS) to dy- namically extract and use the knowledge of context as feedback in order to adaptively choose the appropriate classifiers and thereby enhance the target classification performance. The features are extracted by symbolic dynamic filtering (SDF) from the time series of sensors in an array and spatio-temporal aggregation of these features represents the context. Then, a context evolution model is constructed as a deterministic finite state automata (DFSA) and, for every context state in this DFSA, an event classifier is trained to classify the targets. The proposed technique of detection and classification has been compared with a traditional method of training classifiers without using any contextual information. [Copyright &y& Elsevier]

The control of economical processes is not so well treated in the specialty literature, as far as we know, for systems of higher orders. The models based on the law of supply and demand so far are of the first order only. The interdependencies between various economical agents that are situated in their own ecosystem are the subject approached in this paper, with emphasis on using current day IT possibilities to implement such control systems. [ABSTRACT FROM AUTHOR]

The purpose of the present paper is to determine the best choices for color segmentation techniques as basis for skin detection and to review the main approaches in subsequent texture segmentation. The main goal is to determine which color space offers the best clustering for the skin color space and therefore a compared analysis of the most popular color spaces used in the literature is conducted. The results are statistically processed in order to offer the possibility of signficant conclusions to be drawn. However, the color segmentation alone does not provide eficiently accurate results when it comes to skin detection so it needs to be followed by eficient texture segmentation that would reduce false positives. [ABSTRACT FROM AUTHOR]

Eric Hughes, the gravelly voiced marketing manager for Adaptive Networks Inc. (Cambridge, MA), put a smile on my face recently. Eric's company makes control networks for homes, offices and factories, […]

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The process of submitting a paper to a journal can be incredibly frustrating for authors. Practically everyone who has been active in research for more than a few years has [...]

Of books and bibles I have spent a lot of time during the past 25 years writing computer books and even more time reading computer books written by others. But […]