Data hiding within audio signals by Petrovic, Winograd, Jemili, Metois
By Petrovic, Winograd, Jemili, Metois
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Television audience this day are uncovered to overwhelming quantities of knowledge, and challenged by way of the plethora of interactive performance supplied through present set-top containers. to make sure wide adoption of this know-how by way of shoppers, destiny electronic tv should take usability matters completely into consideration.
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ACSys Data Mining 47 Plug-Ins Visualise VR Cave Workbench GUI Execute XML XML XML Data Extraction Agent Schema Statistical Summary Visualise Associatns Fast Summaries XML Regression Rules Java SEF/JDBC Data Access API Read Many Data Cubes Write Rarely Fast Data Cube Generation (proposed Java SEF) RDBMS Fig. 7. A schematic of the architecture of the Data Miner’s Arcade. Three APIs are identiﬁed, providing access to the data, the GUI interfaces, and output of the models. Whilst there are a number of systems that have provided such a uniﬁed interface to a variety of tools, the architecture of The Data Miner’s Arcade has a number of distinct advantages and features.
All are performing similar and related tasks, yet the results need to be carefully understood in the context of the tool used to generate the results. Tuning them requires diﬀerent interactions with (sometimes very) diﬀerent interfaces, and simply getting the data into a form that the tool can process requires many careful transformations. While sometimes seeming trivial, these are at best annoyances, and at worst they signiﬁcantly inhibit the data mining process. The ﬁrst task in a data mining project, once the data is made available in some format, is to transform that data into a format suitable for each of the data mining tools to be used.
The required modiﬁcation will be discussed brieﬂy, below. In a nutshell, the original MARS is an eﬃcient technique designed to select a (relatively high quality) model from the space of multivariate piecewise linear functions1 . kd (x): K1 Kd ... , d. , Kj are certain prespeciﬁed knot locations on the variable xj taken to be, for example, quantiles of the corresponding marginal distribution of the data points. kd can be determined based the least squares ﬁt of the general model (3) to the data at hand.