tew 648ubm

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Vro username and password (default) (lower)

2120 (Telenet firmware) AdminTelsey cpva502 + (telsey firmware) AdminTenda TWL release R (Tenda firmware) Admin admin AdminThomson speedtouch 530 (Thomson firmware) AdminThomson speedtouch 530 V6 (Thomson firmware) AdminThomson speedtouch 546 V6 (Thomson firmware) AdminThomson tg585 (Thomson firmware) AdministratorTilgin vood 453 W (tilgin German firmware) AdminTomato firmware V1 admin AdminTP-link TD-8810 (TP-LINK firmware) AdminTP-link TD-8817 (TP-LINK firmware) AdminTP-link TD-8840 (TP-LINK

Select an appropriate wireless printer Server

equipment providers, such as network devices, D-Link, TP-LINK, trend, addems, and HP, have launched similar product solutions. This allows LAN users to share a printer in a wireless manner to effectively utilize printer resources, saving the trouble of wiring and making it easy to use. TP-LINK TL-WPS510U Such as TP-LINK reference price 850 yuan) is a single USB port wireless printing server, provide a USB 2.0 printer port, can be compatible with the mainstream USB interface printer on the marke

Hacking the D-Link DIR-890L

:8080/HNAP1$ telnet 1.2.3.4 8080Trying 1.2.3.4...Connected to 1.2.3.4.Escape character is '^]'. BusyBox v1.14.1 (2015-02-11 17:15:51 CST) built-in shell (msh)Enter 'help' for a list of built-in commands. Wget requests will be suspended because cgibin will wait for telnetd to return. The following is an application written in Python: #!/usr/bin/env python import sysimport urllib2import httplib try: ip_port = sys.argv[1].split(':') ip = ip_port[0] if len(ip_port) == 2: port = ip

Module switching: How to Implement Loading

Flex is not that troublesome. Copy codeYou can. How to use code, flex has modulemanager import MX. Events. moduleevent; Import MX. modules. modulemanager; Import MX. modules. moduleloader; Import MX. modules. imoduleinfo; Protected VaR _ moduleinfo: imoduleinfo; Public Function Init (): void { _ Moduleinfo = modulemanager. getmodule ("testm.swf "); // Add some listeners _ Moduleinfo. addeventlistener (moduleevent. Ready, onmoduleready ); _ Moduleinfo. addeventlisten

Machine Learning Basic Knowledge

Componentanalysis), SVD (Singular valuedecomposition singular value decomposition), FA (Factor Analysis factor analytical method).Text Mining (Textual mining):VSM (vectors spacemodel vector space model), Word2vec (Word vector learning model), TF (term frequency frequency), TF-IDF (Termfrequency-inverse Document Frequency Word Frequency-reverse document rate), MI (Mutual information Mutual information), ECE (expected crossentropy desired crossover entropy), Qemi (two information entropy), IG (in

Sensitive information leakage Part2 of reverse router firmware

The various tools for unpacking router firmware are described in detail in the previous article. After unpacking, the files in the firmware are obtained. The next step is to analyze the file for leaks. The objective of this analysis is to trendnet routers, the vulnerability of which is a remote access to router permissions.Preliminary analysisThrough the router's login interface to learn that the router model is TRENDnet TEW-654TR, which is useful to

Three years work experience web front end interview

valuesWhat are the values of the DTD    The advantages and disadvantages of frameWhat are the new features of HTML5, which elements have been removed, and which elements have been addedHTML5 Canvasjquery--Programming Algorithm problemPrint out a inverted triangle 99 multiplication table on the pageFind the number of occurrences of character A in the array [' A ', ' CDA ', [' gfd ', ' Jhgahganbaa ', [' bv ', ' fd ', [' FDA ', ' KLJ '], ' Aiyo '], ' Tew

Output function Cat,sink,writelines,write.table of R language

fileA=c ("one","tew") writelines (A,con=" D:/test.txt", sep="\ t")Problem: The original content is overwritten each time it is called?The 4.write.table () function outputs the contents of the Dataframe to a file.Usage:write.table (x, File = "", append = FALSE, quote = TRUE, Sep = "",eol = "\ n", na = "na", Dec = ".", Row.) Names = True,Col.names = true, Qmethod = C ("Escape", "double"),fileencoding = "")M=matrix (1:12,nrow=3) df=as.data.frame (m) wr

Basic machine learning Algorithms

(Word vector learning model).Dimensionalityreduction (dimensionality reduction):LDA lineardiscriminant analysis/fisher Linear discriminant linear discriminant analysis/fisher linear discriminant, PCA (Principal Component Analysis of principal components), ICA (independentcomponent analysis of independent components), SVD (Singular value decomposition singular value decomposition), FA (factoranalysis factor analysis method).Text Mining (Textual mining):VSM (vector space model), Word2vec (Word ve

Common knowledge points for machine learning & Data Mining

), Word2vec (Word vector learning model).Dimensionalityreduction (dimensionality reduction):LDA lineardiscriminant analysis/fisher Linear discriminant linear discriminant analysis/fisher linear discriminant, PCA (Principal Component Analysis of principal components), ICA (independentcomponent analysis of independent components), SVD (Singular value decomposition singular value decomposition), FA (factoranalysis factor analysis method).Text Mining (Textual mining):VSM (vector space model), Word2v

"Basics" Common machine learning & data Mining knowledge points

(Word vector learning model).Dimensionalityreduction (dimensionality reduction):LDA lineardiscriminant analysis/fisher Linear discriminant linear discriminant analysis/fisher linear discriminant, PCA (Principal Component Analysis of principal components), ICA (independentcomponent analysis of independent components), SVD (Singular value decomposition singular value decomposition), FA (factoranalysis factor analysis method).Text Mining (Textual mining):VSM (vector space model), Word2vec (Word ve

Common machine learning & data Mining Knowledge points "turn"

( Convolutionalneural Network convolutional neural Networks), Word2vec (Word vector learning model).Dimensionalityreduction (dimensionality reduction):LDA lineardiscriminant analysis/fisher Linear discriminant linear discriminant analysis/fisher linear discriminant, PCA (Principal Component Analysis of principal components), ICA (independentcomponent analysis of independent components), SVD (Singular value decomposition singular value decomposition), FA (factoranalysis factor analysis method).T

"Basics" Common machine learning & data Mining knowledge points

Network convolutional neural Networks), Word2vec (Word vector learning model).Dimensionalityreduction (dimensionality reduction):LDA lineardiscriminant analysis/fisher Linear discriminant linear discriminant analysis/fisher linear discriminant, PCA (Principal Component Analysis of principal components), ICA (independentcomponent analysis of independent components), SVD (Singular value decomposition singular value decomposition), FA (factoranalysis factor analysis method).Text Mining (Textual mi

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