Response.ContentType 所有类型

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原创 最新推荐文章于 2026-04-17 04:53:18 发布 395 阅读 AI 写同款· GEO 优化 ›
CVE-2020-0796 SMB远程代码执行漏洞 分析、验证及加固 0x01漏洞描述 CVE-2020-0796是存在于微软服务器SMB协议中的一个“蠕虫化”漏洞，该漏洞未包含 在微软本月发布的补丁中，是在补丁的序言中泄露的。目前微软尚未发布任何技术详情，思科Talos团队和Fortinet公司提供了简短概述，目前尚不清楚该漏洞的补丁何时发布。 Fortinet公司指出，该漏洞是“微软 SMB 服务器中的一个缓冲区溢出漏洞”，严重等级为最高评分，“该漏洞由易... 阅读详情

'ez' => 'application/andrew-inset',
'hqx' => 'application/mac-binhex40',
'cpt' => 'application/mac-compactpro',
'doc' => 'application/msword',
'bin' => 'application/octet-stream',
'dms' => 'application/octet-stream',
'lha' => 'application/octet-stream',
'lzh' => 'application/octet-stream',
'exe' => 'application/octet-stream',
'class' => 'application/octet-stream',
'so' => 'application/octet-stream',
'dll' => 'application/octet-stream',
'oda' => 'application/oda',
'pdf' => 'application/pdf',
'ai' => 'application/postscript',
'eps' => 'application/postscript',
'ps' => 'application/postscript',
'smi' => 'application/smil',
'smil' => 'application/smil',
'mif' => 'application/vnd.mif',
'xls' => 'application/vnd.ms-excel',
'ppt' => 'application/vnd.ms-powerpoint',
'wbxml' => 'application/vnd.wap.wbxml',
'wmlc' => 'application/vnd.wap.wmlc',
'wmlsc' => 'application/vnd.wap.wmlscriptc',
'bcpio' => 'application/x-bcpio',
'vcd' => 'application/x-cdlink',
'pgn' => 'application/x-chess-pgn',
'cpio' => 'application/x-cpio',
'csh' => 'application/x-csh',
'dcr' => 'application/x-director',
'dir' => 'application/x-director',
'dxr' => 'application/x-director',
'dvi' => 'application/x-dvi',
'spl' => 'application/x-futuresplash',
'gtar' => 'application/x-gtar',
'hdf' => 'application/x-hdf',
'js' => 'application/x-javascript',
'skp' => 'application/x-koan',
'skd' => 'application/x-koan',
'skt' => 'application/x-koan',
'skm' => 'application/x-koan',
'latex' => 'application/x-latex',
'nc' => 'application/x-netcdf',
'cdf' => 'application/x-netcdf',
'sh' => 'application/x-sh',
'shar' => 'application/x-shar',
'swf' => 'application/x-shockwave-flash',
'sit' => 'application/x-stuffit',
'sv4cpio' => 'application/x-sv4cpio',
'sv4crc' => 'application/x-sv4crc',
'tar' => 'application/x-tar',
'tcl' => 'application/x-tcl',
'tex' => 'application/x-tex',
'texinfo' => 'application/x-texinfo',
'texi' => 'application/x-texinfo',
't' => 'application/x-troff',
'tr' => 'application/x-troff',
'roff' => 'application/x-troff',
'man' => 'application/x-troff-man',
'me' => 'application/x-troff-me',
'ms' => 'application/x-troff-ms',
'ustar' => 'application/x-ustar',
'src' => 'application/x-wais-source',
'xhtml' => 'application/xhtml+xml',
'xht' => 'application/xhtml+xml',
'zip' => 'application/zip',
'au' => 'audio/basic',
'snd' => 'audio/basic',
'mid' => 'audio/midi',
'midi' => 'audio/midi',
'kar' => 'audio/midi',
'mpga' => 'audio/mpeg',
'mp2' => 'audio/mpeg',
'mp3' => 'audio/mpeg',
'aif' => 'audio/x-aiff',
'aiff' => 'audio/x-aiff',
'aifc' => 'audio/x-aiff',
'm3u' => 'audio/x-mpegurl',
'ram' => 'audio/x-pn-realaudio',
'rm' => 'audio/x-pn-realaudio',
'rpm' => 'audio/x-pn-realaudio-plugin',
'ra' => 'audio/x-realaudio',
'wav' => 'audio/x-wav',
"wmv" : response.contenttype="video/x-ms-wmv"
"wma" : response.contenttype="audio/x-ms-wma"
'pdb' => 'chemical/x-pdb',
'xyz' => 'chemical/x-xyz',
'bmp' => 'image/bmp',
'gif' => 'image/gif',
'ief' => 'image/ief',
'jpeg' => 'image/jpeg',
'jpg' => 'image/jpeg',
'jpe' => 'image/jpeg',
'png' => 'image/png',
'tiff' => 'image/tiff',
'tif' => 'image/tiff',
'djvu' => 'image/vnd.djvu',
'djv' => 'image/vnd.djvu',
'wbmp' => 'image/vnd.wap.wbmp',
'ras' => 'image/x-cmu-raster',
'pnm' => 'image/x-portable-anymap',
'pbm' => 'image/x-portable-bitmap',
'pgm' => 'image/x-portable-graymap',
'ppm' => 'image/x-portable-pixmap',
'rgb' => 'image/x-rgb',
'xbm' => 'image/x-xbitmap',
'xpm' => 'image/x-xpixmap',
'xwd' => 'image/x-xwindowdump',
'igs' => 'model/iges',
'iges' => 'model/iges',
'msh' => 'model/mesh',
'mesh' => 'model/mesh',
'silo' => 'model/mesh',
'wrl' => 'model/vrml',
'vrml' => 'model/vrml',
'css' => 'text/css',
'html' => 'text/html',
'htm' => 'text/html',
'asc' => 'text/plain',
'txt' => 'text/plain',
'rtx' => 'text/richtext',
'rtf' => 'text/rtf',
'sgml' => 'text/sgml',
'sgm' => 'text/sgml',
'tsv' => 'text/tab-separated-values',
'wml' => 'text/vnd.wap.wml',
'wmls' => 'text/vnd.wap.wmlscript',
'etx' => 'text/x-setext',
'xsl' => 'text/xml',
'xml' => 'text/xml',
'mpeg' => 'video/mpeg',
'mpg' => 'video/mpeg',
'mpe' => 'video/mpeg',
'qt' => 'video/quicktime',
'mov' => 'video/quicktime',
'mxu' => 'video/vnd.mpegurl',
'avi' => 'video/x-msvideo',
'movie' => 'video/x-sgi-movie',
'ice' => 'x-conference/x-cooltalk'

 
标签
#xhtml #tiff #xml #rtf #tcl
#xsl
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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); 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