Generating a random key and encrypting it in vault. To generate a key using the PBKDF2 algorithm (or any such algorithm). For encrypting a file I'll input my password and a key will be generated. This key will be used to perform encryption. Now, suppose if I want to decrypt the same file, I have to enter the password ('ABC123') again. The key will be generated again to do decryption. Azure Disk Encryption is integrated with Azure Key Vault to help you control and manage the disk-encryption keys and secrets in your key vault subscription. You can create a key vault or use an existing one for Azure Disk Encryption. Create Key - Create Key. Creates a new key, stores it, then returns key parameters and attributes to the client. The create key operation can be used to create any key type in Azure Key Vault. If the named key already exists, Azure Key Vault creates a new version of the key. There are many ways to create/update Azure Key Vault, such as Azure PowerShell, Azure CLI, REST API, also there are Azure PowerShell and Azure CLI tasks in VSTS build/release. So do it with Azure PowerShell or Azure CLI.
Built this package as a toy challenge to do the following:
The Dynamic Keyword Phrase Generator enables you to plug in your primary, secondary and even tertiary keyword phrases. All you need to do is enter in these keyword phrases, separated by comma (,) into the appropriate fields and click generate below.
1 - Compute the most important key-words (a key-word can be between 1-3 words)
2 - Choose the top n words from the previously generated list. Compare these key- words with all the words occurring in all of the transcripts.
3 - Generate a score (rank) for these top n words based on analysed transcripts.
What this package does:
1 - Generates the keywords (from 1-3 words in length) from a document based, based on the RAKE algorithm
2 - Generate vector representations of all key words and words in a test corpus, using Word2Vec.
3 - Ranks key words by comparing key word vectors with paragraph/document vectors from test corpus
4 - Saves ranked keywords to text file (and/or displays on the console)
Installing dependencies
The code was developed with python 3.5 and requires the following libraries/versions:
gensim2.0.0numpy1.12.1scikit-learn0.18.1wget3.2
These dependencies are specified in requirements.txt, and can be downloaded via the following command:
Usage
Running the keyword_xtract file, will carry out the steps described above (keyword extraction -> compute vector representations -> rank key words)
Models available:
Key Phrase Outline
A truncated version of Google's pre-trained Word2Vec model is available as default. GloVe Word2Vec models (https://nlp.stanford.edu/projects/glove/) can also be downloaded by specifying the model required at run time:
glove_6B - Wikipedia 2014 + Gigaword 5 (6B tokens, 400K vocab, uncased, 50d, 100d, 200d, & 300d vectors, 822 MB download)glove_42B - Common Crawl (42B tokens, 1.9M vocab, uncased, 300d vectors, 1.75 GB download): glove.42B.300d.zipglove_840B - Common Crawl (840B tokens, 2.2M vocab, cased, 300d vectors, 2.03 GB download): glove.840B.300d.zipglove_twitter - Twitter (2B tweets, 27B tokens, 1.2M vocab, uncased, 25d, 50d, 100d, & 200d vectors, 1.42 GB download)
Use the labels above as inputs for the '-m/--model' command line arguments. If the selected model is not present, the model will be downloaded; this may take some time. It is also possible to use custom user-defined Word2Vec models by supplying a path to the model.
NOTE - the default evaluation docs provided for ranking keywords are 3 document pages related to food, which were extracted from Wikipedia. Please provide your own relevant evaluation documents for accurate keyword ranking. Otherwise, keywords can simply be extracted and the ranking scores ignored.
RAKE algorithm + implementation
I modified an existing RAKE implementation to work with Python 3 and different parameters. In this implementation, RAKE does the following:
(i) Generate key word candidates(ii) Computes 'scores' for each candidate. Words are scored according to their frequency and the typical length of a candidate phrase in which they appear.
Originally implemented by: https://github.com/aneesha/RAKEForked from: https://github.com/BelalC/RAKE-tutorial/tree/master
A Python implementation of the Rapid Automatic Keyword Extraction (RAKE) algorithm as described in:Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010). Automatic Keyword Extraction from Individual Documents. In M. W. Berry & J. Kogan (Eds.), Text Mining: Theory and Applications: John Wiley & Sons.
The source code is released under the MIT License.
Click to open this Microsoft document -. Bitlocker recovery key generator for windows 8 free.
Word2Vec + Ranking
Utilising gensim and pre-trained Word2Vec models, keyword vector representations are computed. Vector representations of evaluation documents are computed by taking the average of the word vectors present in a specified document. The pairwise cosine similarity between each keyword vector and evaluation document vector are computed and averaged, giving a single score which can be utilised as a 'rank' for the keyword.
Generate Focus Key Phrase From A Page Examples
Gensim - https://radimrehurek.com/gensim/index.htmlVector represenations of words and phrases - Distributed Representations of Words and Phrases and their Compositionality; Mikolov, Tomas; Sutskever, Ilya; Chen, Kai; Corrado, Greg; Dean, Jeffrey, arXiv:1310.4546