FEATURES OF WINDOWS 10

Characterization of switching windows of an 160-Gb/s all-optical demultiplexer with data base rates of 10 and 40 Gb/s

Theoretical and experimental switchingwindowsfor all-optical demultiplexing from 160 to10Gb/s and 40 Gb/s, using a gain-transparent ultrafast nonlinear interferometer are presented. Based on these results we determined the maximum transmittance and the integrated contrast ratio, measures for switching losses and crosstalk from neighboring channels, respectively. Demultiplexing to 40 Gb/s (as compared to10Gb/s) leads to 6 dB lower maximum transmittance and 3 dB higher integrated contrast ratio (lower crosstalk).Fuente:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=992602&queryText=Windows+10&newsearch=true&searchField=Search_All

Joint semantic utterance classification and slot filling with recursive neural networks

In recent years, continuous space models have proven to be highly effective at omaine processing tasks ranging from paraphrase detection to omaine modeling. These models are distinctive in their ability to achieve generalization through continuous space representations, and compositionality through arithmetic operations on those representations. Examples of such models include feed-forward and recurrent neural network omaine models. Recursive neural networks (RecNNs) extend this framework by providing an elegant mechanism for incorporating both discrete syntactic structure and continuous-space oma and phrase representations into a powerful compositional model. In this paper, we show that RecNNs can be used to perform the core spoken omaine understanding (SLU) tasks in a spoken dialog omain, more specifically omain and intent determination, concurrently with slot filling, in one jointly trained model. We find that a very simple RecNN model achieves competitive performance on the benchmark ATIS task, as well as on a MicrosoftCortanaconversational understanding task.Fuente:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7078634&queryText=cortana&newsearch=true&searchField=Search_All

Obfuscating Windows DLLs

We present two techniques to obfuscate the interfaces between application binaries andWindowssystem DLLs (dynamic-link libraries). The first technique obfuscates the related symbol information in the binary to prevent static analyses from identifying the invoked library functions. The second technique combines static linking with code obfuscation to avoid the external interface altogether, thus preventing dynamic attacks as well. This is done while still maintaining compatibility with multipleWindowsversions, through run-time adaptation of the application. As the first concrete result of this ongoing research, we demonstrate and evaluate the techniques using a proof-of-concept tool applied to a simple test program.

Fuente:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7174807&queryText=(windows+10)&refinements%5B%5D=&ranges%5B%5D=20150727_20150803_Search+Latest+Date&matchBoolean=true&searchField=Search_All_Text