Flubber
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Posts posted by Flubber
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Scuzati double post-ul. De ce?
Author's blog: pi3 blog
Website: http://site.pi3.com.pl/index.html
Postul e prea lung, vorbesc cu kwe.Nu e problema cu postul dublu, in cazul de fata.
Am observat. Imi facuse `figuri` si la thread-ul initial (fara edit-ul respectiv). Este intr-adevar, lung!
Multumesc pentru raspuns.
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Felicitari!
2 oameni ce merita =)
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Sa speram la un `release' catre ... eh ... public?com/blog/2011/11/duqu-analysis-and-detection-tool.html]NSS will make available our own IDA Pro databases and complete reversed code for DuQu to bona fide researchers who wish to perform their own analysis of the code, scripts and dropped files.LE: Foarte interesant, mult hex xD (gluma!): https://raw.github.com/halsten/Duqu-detectors/master/DuquDriverPatterns.py
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Multumesc pentru raspunsuri.
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Uite, acum incep cu un mic proiect. Acest proiect consta in crearea unui "Notepad" care suporta si alte fisiere, cum ar fi: HTML, CSS, JavaScript. Ma rog, ceva de genul asta. Si vreau sa inteleg cum se introduce aceste suporturi pt. tipurile de fisiere de mai sus.
Foloseste functia "Edit" pentru a nu face double post.
In ce limbaj doresti sa programezi acel Policecosmin's "Notepad++"? Eventual poti incepe un thread la "Ajutor".
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Ce sfaturi imi puteti da in legatura cu alegerea unui singur telefon din modelele prezentate mai jos? Alegerea de preferat sa excluda telefoanele cu touchscreen, dar in caz ca un telefon de mai jos cu touchscreen este mult mai fiabil si mai avantajos din mai multe aspecte, nu ezitati sa-l recomandati. Eventual va rog sa dati si un motiv (cat tine bateria etc.)
Multumesc!
Nokia:
- Nokia 5230 Navi - specificatii
- Nokia 5630 XpressMusic - specificatii
- Nokia 5730 XpressMusic - specificatii
Samsung:
- Samsung Wave II - specificatii
- Nokia 5230 Navi - specificatii
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Sursa: Algorithm Tutorials
Autor: lbackstrom [TopCoder Member]
Comment: Desi se aplica pentru TopCoder, tot aduce ... `food for thought'
Introduction
The first step towards an understanding of why the study and knowledge of algorithms are so important is to define exactly what we mean by an algorithm. According to the popular algorithms textbook Introduction to Algorithms (Second Edition by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein), "an algorithm is any well-defined computational procedure that takes some value, or set of values, as input and produces some value, or set of values as output." In other words, algorithms are like road maps for accomplishing a given, well-defined task. So, a chunk of code that calculates the terms of the Fibonacci sequence is an implementation of a particular algorithm. Even a simple function for adding two numbers is an algorithm in a sense, albeit a simple one.
Some algorithms, like those that compute the Fibonacci sequences, are intuitive and may be innately embedded into our logical thinking and problem solving skills. However, for most of us, complex algorithms are best studied so we can use them as building blocks for more efficient logical problem solving in the future. In fact, you may be surprised to learn just how many complex algorithms people use every day when they check their e-mail or listen to music on their computers. This article will introduce some basic ideas related to the analysis of algorithms, and then put these into practice with a few examples illustrating why it is important to know about algorithms.
Runtime Analysis
One of the most important aspects of an algorithm is how fast it is. It is often easy to come up with an algorithm to solve a problem, but if the algorithm is too slow, it's back to the drawing board. Since the exact speed of an algorithm depends on where the algorithm is run, as well as the exact details of its implementation, computer scientists typically talk about the runtime relative to the size of the input. For example, if the input consists of N integers, an algorithm might have a runtime proportional to N2, represented as O(N2). This means that if you were to run an implementation of the algorithm on your computer with an input of size N, it would take C*N2 seconds, where C is some constant that doesn't change with the size of the input.
However, the execution time of many complex algorithms can vary due to factors other than the size of the input. For example, a sorting algorithm may run much faster when given a set of integers that are already sorted than it would when given the same set of integers in a random order. As a result, you often hear people talk about the worst-case runtime, or the average-case runtime. The worst-case runtime is how long it would take for the algorithm to run if it were given the most insidious of all possible inputs. The average-case runtime is the average of how long it would take the algorithm to run if it were given all possible inputs. Of the two, the worst-case is often easier to reason about, and therefore is more frequently used as a benchmark for a given algorithm. The process of determining the worst-case and average-case runtimes for a given algorithm can be tricky, since it is usually impossible to run an algorithm on all possible inputs. There are many good online resources that can help you in estimating these values.
Approximate completion time for algorithms, N = 100
O(Log(N))------------10-7 seconds
O(N)-------------------10-6 seconds
O(N*Log(N))------------10-5 seconds
O(N2)-----------------10-4 seconds
O(N6)-----------------3 minutes
O(2N)-----------------1014 years.
O(N!)------------------10142 years.Sorting
Sorting provides a good example of an algorithm that is very frequently used by computer scientists. The simplest way to sort a group of items is to start by removing the smallest item from the group, and put it first. Then remove the next smallest, and put it next and so on. Unfortunately, this algorithm is O(N2), meaning that the amount of time it takes is proportional to the number of items squared. If you had to sort a billion things, this algorithm would take around 1018 operations. To put this in perspective, a desktop PC can do a little bit over 109 operations per second, and would take years to finish sorting a billion things this way.
Luckily, there are a number of better algorithms (quicksort, heapsort and mergesort, for example) that have been devised over the years, many of which have a runtime of O(N * Log(N)). This brings the number of operations required to sort a billion items down to a reasonable number that even a cheap desktop could perform. Instead of a billion squared operations (1018) these algorithms require only about 10 billion operations (1010), a factor of 100 million faster.
Shortest Path
Algorithms for finding the shortest path from one point to another have been researched for years. Applications abound, but lets keep things simple by saying we want to find the shortest path from point A to point B in a city with just a few streets and intersections. There are quite a few different algorithms that have been developed to solve such problems, all with different benefits and drawbacks. Before we delve into them though, lets consider how long a naive algorithm - one that tries every conceivable option - would take to run. If the algorithm considered every possible path from A to B (that didn't go in circles), it would not finish in our lifetimes, even if A and B were both in a small town. The runtime of this algorithm is exponential in the size of the input, meaning that it is O(CN) for some C. Even for small values of C, CN becomes astronomical when N gets even moderately large.
One of the fastest algorithms for solving this problem has a runtime of O(E*V*Log(V)), where E is the number of road segments, and V is the number of intersections. To put this in perspective, the algorithm would take about 2 seconds to find the shortest path in a city with 10,000 intersections, and 20,000 road segments (there are usually about 2 road segments per intersection). The algorithm, known as Djikstra's Algorithm, is fairly complex, and requires the use of a data structure known as a priority queue. In some applications, however, even this runtime is too slow (consider finding the shortest path from New York City to San Francisco - there are millions of intersections in the US), and programmers try to do better by using what are known as heuristics. A heuristic is an approximation of something that is relevant to the problem, and is often computed by an algorithm of its own. In the shortest path problem, for example, it is useful to know approximately how far a point is from the destination. Knowing this allows for the development of faster algorithms (such as A*, an algorithm that can sometimes run significantly faster than Djikstra's algorithm) and so programmers come up with heuristics to approximate this value. Doing so does not always improve the runtime of the algorithm in the worst case, but it does make the algorithm faster in most real-world applications.
Approximate algorithms
Sometimes, however, even the most advanced algorithm, with the most advanced heuristics, on the fastest computers is too slow. In this case, sacrifices must be made that relate to the correctness of the result. Rather than trying to get the shortest path, a programmer might be satisfied to find a path that is at most 10% longer than the shortest path.
In fact, there are quite a few important problems for which the best-known algorithm that produces an optimal answer is insufficiently slow for most purposes. The most famous group of these problems is called NP, which stands for non-deterministic polynomial (don't worry about what that means). When a problem is said to be NP-complete or NP-hard, it mean no one knows a good way to solve them optimally. Furthermore, if someone did figure out an efficient algorithm for one NP-hard problem, that algorithm would be applicable to all NP-hard problems.
A good example of an NP-hard problem is the famous traveling salesman problem. A salesman wants to visit N cities, and he knows how long it takes to get from each city to each other city. The question is "how fast can he visit all of the cities?" Since the fastest known algorithm for solving this problem is too slow - and many believe this will always be true - programmers look for sufficiently fast algorithms that give good, but not optimal solutions.
Random Algorithms
Yet another approach to some problems is to randomize an algorithm is some way. While doing so does not improve the algorithm in the worst case, it often makes very good algorithms in the average case. Quicksort is a good example of an algorithm where randomization is often used. In the worst case, quicksort sorts a group of items in O(N2), where N is the number of items. If randomization is incorporated into the algorithm, however, the chances of the worst case actually occurring become diminishingly small, and on average, quicksort has a runtime of O(N*Log(N)). Other algorithms guarantee a runtime of O(N*Log(N)), even in the worst case, but they are slower in the average case. Even though both algorithms have a runtime proportional to N*Log(N), quicksort has a smaller constant factor - that is it requires C*N*Log(N) operations, while other algorithms require more like 2*C*N*Log(N) operations.
Another algorithm that uses random numbers finds the median of a group of numbers with an average runtime of O(N). This is a significant improvement over sorting the numbers and taking the middle one, which takes O(N*Log(N)). Furthermore, while deterministic (non-random) algorithms exist for finding the median with a runtime of O(N), the random algorithm is attractively simple, and often faster than the deterministic algorithms.
The basic idea of the median algorithm is to pick one of the numbers in the group at random, and count how many of the numbers in the group are less than it. Lets say there are N numbers, and K of them are less than or equal to the number we picked at random. If K is less than half of N, then we know that the median is the (N/2-K) th number that is greater than the random number we picked, so we discard the K numbers less than or equal to the random number. Now, we want to find the (N/2-K) th smallest number, instead of the median. The algorithm is the same though, and we simply pick another number at random, and repeat the above steps.
Compression
Another class of algorithm deals with situations such as data compression. This type of algorithm does not have an expected output (like a sorting algorithm), but instead tries to optimize some other criteria. In the case of data compression, the algorithm (LZW, for instance) tries to make the data use as few bytes as possible, in such a way that it can be decompressed to its original form. In some cases, this type of algorithm will use the same techniques as other algorithms, resulting in output that is good, but potentially sub-optimal. JPG and MP3 compression, for example, both compress data in a way that makes the final result somewhat lower quality than the original, but they create much smaller files. MP3 compression does not retain every feature of the original song file, but it attempts to maintain enough of the details to capture most of the quality, while at the same time ensuring the significantly reduced file size that we all know and love. The JPG image file format follows the same principle, but the details are significantly different since the goal is image rather than audio compression.
The Importance of Knowing Algorithms
As a computer scientist, it is important to understand all of these types of algorithms so that one can use them properly. If you are working on an important piece of software, you will likely need to be able to estimate how fast it is going to run. Such an estimate will be less accurate without an understanding of runtime analysis. Furthermore, you need to understand the details of the algorithms involved so that you'll be able to predict if there are special cases in which the software won't work quickly, or if it will produce unacceptable results.
Of course, there are often times when you'll run across a problem that has not been previously studied. In these cases, you have to come up with a new algorithm, or apply an old algorithm in a new way. The more you know about algorithms in this case, the better your chances are of finding a good way to solve the problem. In many cases, a new problem can be reduced to an old problem without too much effort, but you will need to have a fundamental understanding of the old problem in order to do this.
As an example of this, lets consider what a switch does on the Internet. A switch has N cables plugged into it, and receives packets of data coming in from the cables. The switch has to first analyze the packets, and then send them back out on the correct cables. A switch, like a computer, is run by a clock with discrete steps - the packets are send out at discrete intervals, rather than continuously. In a fast switch, we want to send out as many packets as possible during each interval so they don't stack up and get dropped. The goal of the algorithm we want to develop is to send out as many packets as possible during each interval, and also to send them out so that the ones that arrived earlier get sent out earlier. In this case it turns out that an algorithm for a problem that is known as "stable matching" is directly applicable to our problem, though at first glance this relationship seems unlikely. Only through pre-existing algorithmic knowledge and understanding can such a relationship be discovered.
More Real-world Examples
Other examples of real-world problems with solutions requiring advanced algorithms abound. Almost everything that you do with a computer relies in some way on an algorithm that someone has worked very hard to figure out. Even the simplest application on a modern computer would not be possible without algorithms being utilized behind the scenes to manage memory and load data from the hard drive.
There are dozens of applications of complicated algorithms, but I'm going to discuss two problems that require the same skills as some past TopCoder problems. The first is known as the maximum flow problem, and the second is related to dynamic programming, a technique that often solves seemingly impossible problems in blazing speed.
Maximum Flow
The maximum flow problem has to do with determining the best way to get some sort of stuff from one place to another, through a network of some sort. In more concrete terms, the problem first arose in relation to the rail networks of the Soviet Union, during the 1950's. The US wanted to know how quickly the Soviet Union could get supplies through its rail network to its satellite states in Eastern Europe.
In addition, the US wanted to know which rails it could destroy most easily to cut off the satellite states from the rest of the Soviet Union. It turned out that these two problems were closely related, and that solving the max flow problem also solves the min cut problem of figuring out the cheapest way to cut off the Soviet Union from its satellites.
The first efficient algorithm for finding the maximum flow was conceived by two Computer Scientists, named Ford and Fulkerson. The algorithm was subsequently named the Ford-Fulkerson algorithm, and is one of the more famous algorithms in computer science. In the last 50 years, a number of improvements have been made to the Ford-Fulkerson algorithm to make it faster, some of which are dauntingly complex.
Since the problem was first posed, many additional applications have been discovered. The algorithm has obvious relevance to the Internet, where getting as much data as possible from one point to another is important. It also comes up in many business settings, and is an important part of operations research. For example, if you have N employees and N jobs that need to be done, but not every employee can do every job, the max flow algorithm will tell you how to assign your N employees to jobs in such a way that every job gets done, provided that's possible. Graduation, from SRM 200, is a good example of a TopCoder problem that lends itself to a solution using max flow.
Sequence comparison
Many coders go their entire careers without ever having to implement an algorithm that uses dynamic programming. However, dynamic programming pops up in a number of important algorithms. One algorithm that most programmers have probably used, even though they may not have known it, finds differences between two sequences. More specifically, it calculates the minimum number of insertions, deletions, and edits required to transform sequence A into sequence B.
For example, lets consider two sequences of letters, "AABAA" and "AAAB". To transform the first sequence into the second, the simplest thing to do is delete the B in the middle, and change the final A into a B. This algorithm has many applications, including some DNA problems and plagiarism detection. However, the form in which many programmers use it is when comparing two versions of the same source code file. If the elements of the sequence are lines in the file, then this algorithm can tell a programmer which lines of code were removed, which ones were inserted, and which ones were modified to get from one version to the next.
Without dynamic programming, we would have to consider a - you guessed it - exponential number of transformations to get from one sequence to the other. As it is, however, dynamic programming makes for an algorithm with a runtime of only O(N*M), where N and M are the numbers of elements in the two sequences.
Conclusion
The different algorithms that people study are as varied as the problems that they solve. However, chances are good that the problem you are trying to solve is similar to another problem in some respects. By developing a good understanding of a large range of algorithms, you will be able to choose the right one for a problem and apply it properly. Furthermore, solving problems like those found in TopCoder's competitions will help you to hone your skills in this respect. Many of the problems, though they may not seem realistic, require the same set of algorithmic knowledge that comes up every day in the real world.
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Un cosmar al calculatorului?
Viseaza calculatorul...
01101001 01101110 00100000 01101000 01101001 01100010 01100101 01110010 ...
[ ... ]
... 01101110 01100001 01110010 011001012
© sickness 2011
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problema e ca il am testa pe un root si imi arata urmatoare problema
:cum trebuie folosit
ca am incercat mai multe forme dar nu stiu sa il folosescNu iti mai bate capul, il las ca si PoC fiindca nu `functioneaza` cum trebuie.
Daca tot vrei sa iti faci nervi (vazandu-l cand se blocheaza in vreun loop sau nu extrage corect datorita variatiilor din codul sursa, dar si a codului prost scris):
python <numescript>.py -h
exemplu:
python peteava.py -u 3 -f log1 -v 1
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[!] Atentie! Acest program odata executat va face multe request-uri catre site-ul tinta. Nu rulati de pe calculatorul propriu pentru a nu fi catalogat drept `hacker de carton` ce vrea sa faca DoS.
Later edit:
Well, dupa o zi de coding, am ajuns la concluzia ca l-am scris de cacat (se blocheaza in anumite loop-uri, uneori nu extrage corect datele s.a.m.d.), asa ca singurul motiv pentru care il las ar fi analizarea lui.
#!/usr/bin/env python
# -- - - - - - - - --
# Coded in Python 2.7
# Acest script verifica paginile disponibile de pe www.peteava.ro categoria `Anime`
# Determina care este ultima pagina, iar apoi itereaza prin fiecare pagina luand
# codul sursa pentru embed, generand intr-un final o pagina html cu toate
# embed-urile.
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
print "
[*] Procesez configurarile ... te rog asteapta.\n"
try:
import urllib2
except:
print "[-] Lipseste modulul \"urllib2\".\n"
print ImportError
exit(0)
try:
import os
except:
print "[-] Lipseste modulul \"os\".\n"
print ImportError
exit(0)
try:
from optparse import OptionParser
except:
print "[-] Functia \"OptionParser\" din modulul \"optparser\" nu a fost gasita.\n"
print ImportError
exit(0)
try:
from time import strftime
except:
print "[-] Functia \"strftime\" din modulul \"time\" nu a fost gasita.\n"
print ImportError
exit(0)
# Configuram parser-ul de argumente
args = OptionParser()
# -- -- -- -- -- -- -- -- -- -- -- --
args.add_option("-u", "--user-agent", help="User-agent:\n\t(default: 1)\n\t1. Mozilla Firefox 6.0;\n\t2. Internet Explorer 9.0;\n\t3. Opera 11.11;\n\t4. Chrome 15.0.872.0.", dest = "useragent", type="int")
args.add_option("-f", "--file", help="Fisierul HTML in care vor fi salvate codurile embed (default: log_<data curenta a sistemului ZZ.LL.AAAA_O.M.S>.html", dest = "obiect", type="string")
args.add_option("-v", "--verbose", help="Verbose? (default: 0); 1 - setat / 0 - nu este setat", dest = "verbose", type="int")
# -- -- -- -- -- -- -- -- -- -- -- --
(opts, args) = args.parse_args()
# - - - - - - - - - - - - - - - - - -
useragent = opts.useragent
verbose = opts.verbose
fobject = opts.obiect
# -- -- -- -- -- -- -- -- -- -- - - -
timestamp = "_" + str(strftime("%d.%m.%Y_%H.%M.%S")) + ".html"
if opts.verbose is None:
print "
[*] Nu s-a specificat o valoare anume pentru verbosity. Folosesc default: 0 (nu este setat).\n"
opts.verbose = 0
if opts.obiect is None:
print "
[*] Nu s-a specificat o valoare pentru optiunea \"-f/--file\". Folosesc default: log_<data curenta a sistemului ZZ.LL.AAAA_O.M.S>.html.\n"
opts.obiect = str('log'+str(timestamp))
if opts.useragent is None:
print "
[*] Nu s-a specificat user-agent-ul. Am sa folosesc user-agent prestabilit: Mozilla Firefox 6.0\n"
useragent = "Mozilla/5.0 (Windows NT 6.1; rv:6.0) Gecko/20110814 Firefox/6.0"
elif opts.useragent == 1:
print "[+] Am setat user-agent: Mozilla/5.0 (Windows NT 6.1; rv:6.0) Gecko/20110814 Firefox/6.0\n"
useragent = "Mozilla/5.0 (Windows NT 6.1; rv:6.0) Gecko/20110814 Firefox/6.0"
elif opts.useragent == 2:
print "[+] Am setat user-agent: Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)\n"
useragent = "Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)"
elif opts.useragent == 3:
print "[+] Am setat user-agent: Mozilla/5.0 (Windows NT 5.1; U; en; rv:1.8.1) Gecko/20061208 Firefox/5.0 Opera 11.11"
useragent = "Mozilla/5.0 (Windows NT 5.1; U; en; rv:1.8.1) Gecko/20061208 Firefox/5.0 Opera 11.11"
elif opts.useragent == 4:
print "[+] Am setat user-agent: Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.2 (KHTML, like Gecko) Chrome/15.0.872.0 Safari/535.2"
useragent = "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.2 (KHTML, like Gecko) Chrome/15.0.872.0 Safari/535.2"
# - END - - - - - - - - - - - - - - - -
prim = []
doi = []
trei = []
ids = []
idsf = []
ultimp = ""
def pagina():
# Inainte de a stoca id-urile disponibile, stabilim care este ultima pagina ce o putem accesa
req = urllib2.Request("http://www.peteava.ro/browse/categoria/102/pagina/1")
# Configuram `browser-ul` nostru
req.add_header('User-agent', useragent)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - -
print "[+] Configurarile au fost procesate cu succes.\n"
# - - - - - - - - - - - - - - - - - - - - - - - - - - - -
upag = urllib2.urlopen(req)
upag = upag.read()
intgeta = upag.split()
intgetb = intgeta.index("if(page_bottom")+2
intAnte = intgeta[intgetb:intgetb+1]
for a in intAnte:
x = a.split(")")
x.pop(1)
for y in x:
ultimp = y
print "[+] Am detectat ultima pagina ca fiind: " + '"' + str(ultimp) + '"' + ".\n"
# - END - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Luam toate `id-urile` de pe pagina si le stocam intr-o lista
def extractie():
pagina()
iterare = 1
print "[+] Incep `extractia` ... \n[!] Apasa Control-C (^C) pentru a intrerupe procesul.\n"
while iterare <= ultimp:
try:
urlsortare = "http://www.peteava.ro/browse/categoria/102/pagina/" + str(iterare)
req = urllib2.Request(urlsortare)
req = urllib2.urlopen(req)
stocare = req.read()
exa = stocare.split()
for a in exa:
if "href=\"/id-" in a:
b = a[6:]
prim.append(
else:
pass
for a in prim:
a = a.replace('"', "")
doi.append(a)
trei = set(doi)
for a in trei:
if verbose == 1:
print "
[*] Am adaugat id: " + str(a)
ids.append(a)
else:
ids.append(a)
iterare += 1
except KeyboardInterrupt as ki:
print "\n[Control-C] Keyboard interrupt. Inchei procesul.",ki
break
exit(0)
# - END - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Deschidem pagina HTML in care vom salva codurile embed
def procesare():
try:
extractie()
except (IOError, SystemExit) as eroare:
print "[-] Ceva a intervenit."
raise eroare
exit(0)
fisier = str(fobject) + str(timestamp)
print "[+] Creez log-ul html: \"" + str(fisier) + "\".\n"
fobj = open(fisier, "a")
fobj.write('<html>\n<title>Generat la data de ' + str(strftime("[%A] %d.%m.%Y %H:%M:%S")) + '</title>\n<body>\n<script type="text/javascript" language="javascript">\n\n')
urlembed = "http://www.peteava.ro"
pattern = "value=\"<object"
count = 1
eachdata = ""
fstring = ""
finalstring = ""
idsf = set(ids)
try:
for eid in idsf:
req = urllib2.Request(urlembed+str(eid))
req = urllib2.urlopen(req)
stocare = req.read()
exa = stocare.split()
for a in exa:
if pattern in a:
patternid = exa.index(pattern)
data = exa[patternid+1:patternid+24]
for dataa in data:
fstring = fstring + ' ' + dataa
finalstring = fstring.replace("<", "<").replace(">", ">").replace(""", '"').replace("&", "&")
if verbose == 1:
print "[+] Am scris urmatoarele: \"<object" + str(finalstring[:-4]) + "\" in fisierul: \"" + str(fisier) + "\"\n"
fobj.write("var video_embed_code" + str(count) + " = <object" + str(finalstring[:-4])+"\n")
count += 1
else:
fobj.write("var video_embed_code" + str(count) + " = <object" + str(finalstring[:-4])+"\n")
count += 1
else:
pass
fstring = ""
finalstring = ""
except (KeyboardInterrupt, IOError, SystemExit) as eroare:
print "[-] Ceva a intervenit."
raise eroare
exit(0)
fobj.write("\n</script>\n</body>\n</html>")
fobj.close()
print "[+] Am terminat.\n"
# - END - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
if __name__ == '__main__':
print "[+] Am pornit la data de: " + str(strftime("[%A] %d.%m.%Y %H:%M:%S")) + ".\n"
procesare()sau Python code - 205 lines - codepad
Testat pana la pagina a 3-a, nu sunt dupes.
Testati-l si voi si reveniti cu feedback.
Programat datorita faptului ca l-am luat sub forma de challenge.
Pe viitor, loc de imbunatit:
- clase
- multithreading
- exceptii si mai friendly
- mai multe argumente (timeout la conexiuni s.a.m.d.)
- ??
Astept critici constructive, s.a.m.d.
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Si nu in ultimul rand invata sa folosesti manualul!
Pentru orice program caruia nu ii stii sintaxa (cum ar fi "ls") sau nu esti familiarizat/<insereaza mai multe motive aici>, tastezi in terminal:
man <comanda>
ex: man lsSi iti spune ce a mancat ieri, cati ani are ma-sa si ce o sa faca in viitor cand va fi mare!
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Details
[B]Programs[/B]: HTML Editor, Photoshop
[B]Difficulty[/B]: Intermediate - Advanced
[B]Completion Time[/B]: 3-6 hoursSource files: http://nettuts.s3.amazonaws.com/017_Creatif/Site_Download.zip
Demo: Creatif
What you will learn?
Today I’m going to take you through my entire process of getting from Photoshop to completed HTML. We’re going to build out a set of 4 PSD mockups of a website that eventually will become a WordPress theme. It’s a massive tutorial, so if you’re going to follow through to the end, make sure you have a few hours to spare!
Credits: net.tutsplus.com
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Un link cu un exemplu de casti de unde se pot cumpara?
LATER EDIT:
Casti cu microfon A4Tech HS-28, Negru HS-28 - eMAG.ro
Bune astea?
Specifications
.Frequency Response: 20Hz~20KHz -- e ok
.Nominal Impedance: 32ohms
.Microphone Sensitivity: -58DB
.Sensitivity: 105dB
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Parca a mai postat cineva cu ceva timp in urma cursurile astea, ca le urmasem eu din cauza acelui topic :3 Search before posting
http://rstcenter.com/forum/27180-cs50-computer-science-50-harvard-course.rst
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Pentru cei ce folosesc sisteme *NIX RPM-based:
yum install pycrypto
Daca nu vi-l gaseste, verificati-va repo-urile din /etc/yum.repos.d/.
Eventual puteti incerca sa cautati pachetul PyCrypto:
[^]
yum search python | grep crypto
Repo-urile mele:
~ yum repolist
repo id repo name status
fedora Fedora 14 - i386 17,883
-- cut --
rpmfusion-free RPM Fusion for Fedora 14 - Free 364
rpmfusion-free-updates RPM Fusion for Fedora 14 - Free - Updates 788
rpmfusion-nonfree RPM Fusion for Fedora 14 - Nonfree 176
rpmfusion-nonfree-updates RPM Fusion for Fedora 14 - Nonfree - Updates 418
updates Fedora 14 - i386 - Updates 7,750
repolist: 27,736[^] executata pe Fedora Core 14 (Linux 2.6.35.14-96.fc14.i686 | i686 i686 i386 GNU/Linux):
~ yum search python | grep crypto
python-crypto.i686 : Cryptography library for Python
m2crypto.i686 : Support for using OpenSSL in python scripts
pycryptopp.i686 : Python wrappers for the Crypto++ libraryHave fun si haideti sa imbunatatim codul (fix bug(s) etc.) si sa continuam munca (enorma) depusa de cmiN pentru noi.
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alsamixer
in CLI, si asigura-te ca ai level maxim la "<stuff> mic"
unde <stuff> poate fi: Front, bla bla
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E amuzant cum fiecare dau sfaturi, unii trecuti prin fel si fel de experiente. Singurul mod in a afla ce trebuie sa faci si sa iti dai seama unde gresesti este sa te intrebi. Prima oara intelege situatia, nu sta speriat in starea aceea ce iti blocheaza mintea de la a gandii mai departe (in stare de conflict, creierul impune 2 decizii pe moment, fight or flight asta in timp ce inima iti pompeaza adrenalina ca nebuna, motivul pentru care iti bate ca dracu si respiri `ciudat' cand e de bataie). Intreaba-te ce se intampla, de unde a pornit totul si de ce se comporta asa. Incearca sa le intelegi modelul comportamentului, iar daca pana acum nu ai rezolvat cu politie, director s.a.m.d. singurul `sfat' ce ti-l pot da, este sa incerci sa prinzi incredere in tine desi probabil iti este greu. Faptul ca fugi atunci cand ei vin la tine este similar momentului in care un caine iti arata agresivitate, iar tu fugi. Ii trezesti si `antrenezi' simtul de vanatoare. Nu te baza pe nimeni din jur sperand ca o sa iti sara in ajutor atunci cand iti iei pumni in falca, ce ti s-a spus pana acum de increderea personala e valabil, iar asa cum s-a mai mentionat, tu stii cel mai bine `in ce ape te scalzi'. Fiindca esti primul an, o sa mai dureze, poate chiar mai mult, dar incearca sa ai o gasca ce se impune si stai mai mult cu ea (desi ai mentionat ca nu ai prieteni, formeaza-ti, dar nici sa fi `frate' cu ei). Daca esti la ei in cartier, si ai de gand sa ii arzi cu alti baieti ce nu iti sunt aproape si a doua zi, gandeste-te de doua ori, fiindca tot pe aceeasi poarta vei intra. Am fost in situatia ta cu tiganii din cartierul unde este situat liceul, i-am alergat, m-au alergat si tot asa, dar nu mi-am luat niciodata bataie fiindca am avut grija sa ma inteleg cu toata lumea, si cu cei ce gandesc, si cu cei ce nu gandesc, in timp s-au obisnuit cu fata mea si prezenta mea. Alte sfaturi, nu stiu, eventual foloseste-ti creierul!
P.S.: Tot ce am spus mai sus este bullshit daca ai sa crezi pur si simplu. Nu inghitii totul asa cum ti se da ci incearca sa te intrebi daca s-ar aplica situatiei tale, iar daca da, testeaza (s-ar putea sa doara uneori daca iti iei pumni si picioare).
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Calculus nu este de fapt sinonim cu analiza matematica ?
Din cateva mici cautari se pare ca calculus si analiza matematica se refera la aceeasi chestieanaliza matematica
si calculus
Deci tind sa cred ca denumirea de calculus nu este altceva decat denumirea in engleza a aceleiasi ramuri.
Foarte corect, nu am observat asta, multumesc. Dar totusi... calculus este din latina, si nu are `conotatii' din engleza. Insa da, se foloseste `analiza matematica' si nu `calculus'.
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old school VS new school
in Off-topic
Posted
Ca tot venii vorba de old school, HackThisSite(.org) au introdus noi challenge-uri de Phone Phreaking[1], punand in picioare un PBX. Necesita fie un simulator (duh, easy nowadays cu python) sau construirea fizica a cutiilor[2] (blue, red, black etc.).
[1] Hack This Site!
[2] Category:Phreaking boxes - Wikipedia, the free encyclopedia
Sa nu uitam, frecventa tonului magic, 2600(hz).
`Respect to the old school, knowledge and ego.`