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No detailed description available for \"Encyclopaedic Dictionary of Information Technology and Systems\".

Over 10,000 Detailed Entries!\"There is a myth that all stakeholders in the healthcare space understand the meaning of basic information technology jargon.In truth, the vernacular of contemporary medical information systems is unique, and often misused or misunderstoodÖ Moreover, an emerging national Heath Information Technology (HIT).

No detailed description available for \"Encyclopaedic Dictionary of Information Technology and Systems\".

No detailed description available for \"Multilingual Dictionary of IT Security\".

The Dictionary of Information Security is a compilation of security terms and definitions that working security professionals and IT students will find helpful.IT professionals and IT students will find this a handy reference to help them identify terms used in practice, in journals and articles, and on websites. The dictionary has complete coverage of security terms and includes cutting-edge technologies and newer terminology only now becoming accepted use amongst security practitioners. Certification candidates for security specializations like CISSP and Security+ will also find this a valuable resource.* Your one stop shop coverage of malware, wireless technologies, and phishing *An easy to use tol featuring the ability to cross references makeing navigation easy* Includes special coverage of military and government terms for the latest hot topics

The BCS Glossary is the most authoritative and comprehensive glossary of its kind on the market today. This unrivalled study aid and reference tool has newly updated entries and is divided into themed sections, making it more than just a list of definitions. Written in a style that is easily accessible to anybody with an interest in computing, it is specifically designed to support those taking computer courses or courses where computers are used, in schools and Further Education colleges.

We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of 'culturomics,' focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities.

We demonstrate a simple connection between dictionary methods for time series classification, which involve extracting and counting symbolic patterns in time series, and methods based on transforming input time series using convolutional kernels, namely Rocket and its variants. We show that by adjusting a single hyperparameter it is possible to move by degrees between models resembling dictionary methods and models resembling Rocket. We present Hydra, a simple, fast, and accurate dictionary method for time series classification using competing convolutional kernels, combining key aspects of both Rocket and conventional dictionary methods. Hydra is faster and more accurate than the most accurate existing dictionary methods, achieving similar accuracy to several of the most accurate current methods for time series classification. Hydra can also be combined with Rocket and its variants to significantly improve the accuracy of these methods.

The employed dictionary plays an important role in sparse representation or sparse coding based image reconstruction and classification, while learning dictionaries from the training data has led to state-of-the-art results in image classification tasks. However, many dictionary learning models exploit only the discriminative information in either the representation coefficients or the representation residual, which limits their performance. In this paper we present a novel dictionary learning method based on the Fisher discrimination criterion. A structured dictionary, whose atoms have correspondences to the subject class labels, is learned, with which not only the representation residual can be used to distinguish different classes, but also the representation coefficients have small within-class scatter and big between-class scatter. The classification scheme associated with the proposed Fisher discrimination dictionary learning (FDDL) model is consequently presented by exploiting the discriminative information in both the representation residual and the representation coefficients. The proposed FDDL model is extensively evaluated on various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods in a variety of classification tasks.